Why We Need to Bet on Agents Now

Let’s cut through the noise. Agents, these AI-driven digital workers, aren’t some sci-fi fantasy. They’re here, and they’re about to fundamentally change how you go about your day and how your business operates. Whether you’re building products, running marketing campaigns, or supporting operations or clients, understanding agents is no longer optional. It’s the key to getting and staying ahead.

Agents Are No Longer Theoretical

My prediction is that in the near future, agents will be indispensable. People won’t monitor their email. They won’t browse social media or use apps and websites as they do today. Their agents will do these tasks for them. These AI-driven workers will curate and deliver exactly what users need, without requiring them to use third-party user interfaces. We won’t have to log into Instagram or email. Our agent can stream email and content from other services through a single interface.

This will change marketing because marketers will have to learn how to attract agents to reach their human operators. Online stores will have to learn how to sell to agents. Agents make purchases on behalf of their human operators. Websites and apps won’t target humans but agents. If it can be done on a computer, agents will be able to do it. This includes phones. We need to rethink target users across our products. Our world will go through an epic paradigm shift.

Agents are still an emerging concept, and nothing is real or set in stone yet. However, early movers are already deploying agents. They use them to automate tasks, generate content, write code, and optimize decision-making. But here’s the kicker, most businesses don’t yet have agents tailored to their unique needs. This presents a massive opportunity. The potential applications are vast, and the market is wide open. If you get started today, we’re not just building agents; we’re writing the best practices for this transformation. By focusing on how to attract and build agents now, we’re positioning ourselves to thrive as the agent ecosystem grows.

This is our chance to step up as experts. Yes, we’re in uncharted territory, but that’s a good thing. I have made predictions here. However, no one really knows what’s coming. No one knows what to do to apply agents in industries. We’re not just building agents; we’re shaping the best practices that will define agents in our respective industries.

Why Early Adoption Matters

Being early comes with risks, but the opportunities and reward far outweigh them. By diving in now, we can shape the future of how agents are built, delivered, and operated. Early adoption means gaining:

  • Experience: Each agent we develop is a chance to learn from both success and failure. What works, what doesn’t, and how to pivot.
  • Credibility: As agents become mainstream, businesses will seek pioneers, those who’ve already proven their expertise and early results.
  • Market Advantage: Agents are self-improving. If we start soon, we will develop smarter and more capable agents sooner. Our systems will perform better compared to late adopters. Compounded learning will separate leaders from laggards. By diving in now, we gain an early entrance advantage in terms of experience and credibility. We also gain a head start in acquiring the precious data we need. This data is essential to feed our agents and improve their performance.

The Work Ahead

We must learn to build agents. We must also understand how to deliver and operate them as the best solution for specific use cases.

Delivering Agents

  • Planning: Understand the jobs to be done. Identify use cases, workflows, and challenges where agents can provide meaningful value.
  • Designing: Define clear objectives, user interactions, and system integration and interfaces for the agent.
  • Building: Train agents on the right data, using AI frameworks that allow flexibility and growth.
  • Testing and Iterating: Rigorously evaluate agent performance and refine based on real-world feedback.
  • Deploying: Introduce agents thoughtfully, ensuring seamless onboarding and integration with existing tools and workflows.
  • Releasing: Equip users with proper training and documentation to ensure successful adoption.

Operating Agents

  • Managing: Overseeing the agent’s functionality, ensuring it runs as expected, and addressing any operational issues.
  • Monitoring: Tracking real-time performance metrics, such as speed, accuracy, and user feedback, to ensure consistent quality.
  • Evaluating: Regularly reviewing the agent’s outcomes against its goals, identifying areas for improvement or additional training.
  • Improving: Iterating on the agent. This involves refining its prompts, templates, tools, and algorithms. We can update its RAG with new data. We can fine-tune it or retrain it with new data. We can also enhance its features to adapt to evolving needs.

Roadmap

Our roadmap to be successful with agents as a product focuses on both strategic insights and actionable steps.

  1. Understand the Jobs to Be Done: Not every task needs an agent, and replacing traditional digital solutions (e.g., websites or apps) requires clear benefits.
  2. Iterate Relentlessly: The first version of any agent won’t be perfect. It may often hallucinate and get things wrong. That’s fine. What matters is how quickly we learn and adapt.
  3. Collaborate Across Teams: Product, marketing, and support teams must all contribute. Everyone’s input is critical. The more perspectives we have, the better equipped we are to design and refine agents that excel.
  4. Measure and Optimize: Agents need monitoring and fine-tuning. Metrics like accuracy, speed, and user satisfaction will guide us.

Agents Improve Over Time

Let’s tackle a key truth, the first iteration of any agent will rarely deliver perfect results. Early versions might be clunky, prone to hallucinations, errors, or lacking the nuanced judgment needed for complex tasks. But that’s not a failure. It marks the beginning of an iterative process. This process allows agents to learn, adapt, and improve through data and feedback.

Unlike traditional solutions, which typically rely on fixed algorithms and human-driven updates, agents can operate dynamically. They evolve in real-time as they encounter new data and scenarios. This ability to self-optimize positions agents as uniquely suited for complex and evolving challenges where traditional solutions fall short.

  • Initial Challenges: In their infancy, agents might struggle with insufficient data, unclear objectives, or unexpected scenarios. These early hiccups can result in inconsistent performance or even outright errors.
  • Continuous Learning: With every iteration, agents refine their capabilities. New data helps them understand patterns better, adapt to edge cases, and make more accurate decisions. The more they’re used, the smarter they get.
  • Operator Involvement: Effective improvement requires skilled operators. We monitor agent performance. We analyze results and provide feedback and data. In doing so, we ensure agents evolve in ways that align with business goals.
  • Replacing Traditional Solutions: Over time, agents become faster. They become more accurate and better tuned to tasks. Eventually, they will outperform traditional solutions and humans. This transformation won’t happen overnight, but the incremental improvements lead to exponential results. Starting early helps us get through this journey faster than late adopters.

The goal isn’t perfection from day one. It’s about building a foundation that grows stronger and more capable with time.

A Vision for What’s Next

Agents will handle the tedious, time-consuming stuff, freeing us to focus on strategy, creativity, and big-picture thinking. Our clients see the results. Our stakeholders see the value. We get to lead the charge in one of the most exciting shifts in tech.

But this won’t happen by accident. It’s going to take the courage to move now with bold ideas and hard work. Its going to take a willingness to fail fast and learn faster. Let’s embrace the challenge and make it happen.

Let’s get to work! Do you want to talk about how to start or improve your agentic ops journey, I’m here.

First Principles of Business Operations Systems and Applications

Following up the post on “Key Thinkers in the First Principles of Business Operations,” I am continuing the theme of the first principles of business operations. In this post we are going to discuss important systems that are helping to shape and innovate on the principles. Innovation in business operations today are driven by transformative applications and breakthrough systems that have reshaped industries. These systems optimize efficiency, scalability, and intelligence, making businesses more adaptable and resilient. Below are the most impactful applications and systems that embody the first principle of business operations.

1. Value Creation

✅ Creating meaningful value for customers through innovation.

  • Apple Ecosystem (iOS, App Store, macOS) – Inspired by Steve Jobs’ focus on customer-centric innovation, Apple created an integrated digital ecosystem that enables businesses to innovate and distribute products globally.
  • OpenAI (ChatGPT, DALL·E, Codex) – Driven by first-principles thinking, OpenAI democratized AI-powered creativity and automation, expanding possibilities in content creation and software development.
  • Stripe – Reflecting Clayton Christensen’s Disruptive Innovation model, Stripe simplifies online payments, lowering the barrier to entry for businesses and enabling new digital-first business models.

2. Value Delivery

✅ Ensuring value reaches customers efficiently.

  • Amazon Logistics (AWS, Fulfillment Centers, Prime) – Built around Jeff Bezos’ obsession with customer experience, Amazon redefined logistics, fulfillment, and last-mile delivery using AI-driven efficiency.
  • Shopify – Following lean delivery principles inspired by Taiichi Ohno, Shopify enables businesses to quickly launch and optimize digital storefronts with integrated payment and logistics solutions.
  • FedEx & UPS AI Logistics – Uses machine learning for predictive routing, optimizing package deliveries at a global scale.

3. Revenue Generation

✅ Monetizing value through scalable business models.

  • Salesforce – Reinvented enterprise software with the SaaS (Software-as-a-Service) model, reflecting Marc Andreessen’s software-first revenue approach.
  • Netflix Recommendation AI – Inspired by Reed Hastings’ innovation in subscription-based revenue, Netflix uses AI-driven personalization to maximize content engagement and retention.
  • Maxio (formerly Chargify) – Automates subscription revenue tracking, MRR forecasting, and financial analytics, essential for modern recurring revenue models.

4. Cost Efficiency

✅ Reducing waste and improving operational efficiency.

  • AWS & Cloud Computing (Azure, GCP) – Following Andrew Grove’s efficiency principles, cloud computing transformed IT cost structures, scaling computing power on demand.
  • Lean Six Sigma AI Tools – Inspired by Jack Welch’s cost-cutting efficiency methods, AI-driven process automation reduces waste and improves quality control.
  • Robotic Process Automation (UiPath, Automation Anywhere) – Automates repetitive workflows, reducing labor costs while improving accuracy, reflecting Sam Walton’s obsession with retail efficiency.

5. Process Optimization

✅ Improving workflows for maximum efficiency.

  • Toyota Production System (TPS) – Developed under Taiichi Ohno’s Lean Manufacturing, TPS revolutionized just-in-time production and workflow optimization.
  • Zapier & Make (formerly Integromat) – Following Eliyahu Goldratt’s Theory of Constraints, these tools automate repetitive tasks, streamlining workflows and eliminating bottlenecks.
  • Microsoft Power Automate – Enables process automation at scale, reducing human intervention and optimizing business workflows.

6. Cash Flow Management

✅ Maintaining liquidity and financial stability.

  • QuickBooks & Xero – Following Benjamin Graham’s emphasis on financial discipline, these tools automate cash flow tracking, invoicing, and expense management.
  • Maxio & Stripe Revenue Recognition – Implements Ray Dalio’s principles of risk-adjusted financial planning, providing AI-powered revenue analytics.
  • AI-driven Financial Forecasting (Palantir, Anaplan) – Uses machine learning to predict cash flow trends, mirroring Aswath Damodaran’s financial valuation models.

7. Risk Management

✅ Minimizing uncertainty and protecting business continuity.

  • Riskified – AI-powered fraud detection for e-commerce, applying Nassim Taleb’s risk assessment and antifragility principles.
  • Cybersecurity AI (Darktrace, CrowdStrike) – Uses machine learning to detect and prevent cyber threats, aligning with Howard Marks’ risk-adjusted decision-making.
  • Monte Carlo Simulation Software – Predicts financial and operational risks, a practical application of Jim Collins’ SMaC (Specific, Methodical, and Consistent) strategy.

8. Scalability

✅ Expanding business operations without breaking systems.

  • Kubernetes & Docker – Reflecting Eric Schmidt’s push for cloud-native architecture, these enable businesses to scale infrastructure dynamically.
  • AWS Lambda & Serverless Computing – A realization of Elad Gil’s startup scaling strategies, serverless computing eliminates infrastructure complexity.
  • Notion & Airtable – No-code tools that scale business operations, aligning with Reid Hoffman’s Blitzscaling principles.

9. People & Culture

✅ Enhancing workforce productivity and collaboration.

  • Workday & BambooHR – AI-powered HR and workforce management, reflecting Laszlo Bock’s modern people operations principles.
  • Lattice & CultureAmp – Optimizes employee engagement and performance analytics, driven by Patrick Lencioni’s organizational health framework.
  • Microsoft Teams & Slack – AI-assisted collaboration platforms, embodying Simon Sinek’s vision for purpose-driven teamwork.

10. Decision Intelligence

✅ Making data-driven decisions with precision.

  • Palantir AI Decision Systems – Analyzes vast datasets for strategic decision-making, applying Daniel Kahneman’s cognitive bias research.
  • Google DeepMind AlphaFold – Uses AI to solve complex decision-making challenges, embodying Michael Porter’s structured strategy framework.
  • IBM Watson – AI-powered decision intelligence for business, finance, and healthcare, applying Richard Thaler’s Nudge Theory.

11. Customer Focus

✅ Enhancing customer experience through AI-driven engagement.

  • Zendesk & HubSpot CRM – AI-powered customer service automation, implementing Don Peppers & Martha Rogers’ One-to-One Marketing.
  • Salesforce Einstein AI – Uses AI to personalize customer interactions, following Tony Hsieh’s legendary customer-first philosophy.
  • Amazon Alexa & Google Assistant – AI-driven voice interaction systems, refining Shep Hyken’s principles of customer loyalty.

12. Continuous Improvement

✅ Adapting and iterating for long-term success.

  • Jira & Asana – Agile project management platforms, aligning with Eric Ries’ Lean Startup methodology.
  • A/B Testing AI (Optimizely, Google Optimize) – Uses machine learning to test and optimize business strategies, inspired by James Clear’s Atomic Habits approach.
  • AI-powered KPI Dashboards (Tableau, Power BI) – Continuously monitors performance, applying Kaoru Ishikawa’s quality improvement frameworks.

Final Thoughts: Systems Driving the Future of Business Operations

These cutting-edge applications and systems are transforming business operations by leveraging first-principles thinking, automation, and AI-driven decision-making.

As AgenticOps evolves, these technologies will continue to optimize efficiency, improve decision-making, and scale businesses beyond traditional limits.

💡 What are the most impactful systems in your business today? Need help to improve the impact of your business operating systems, I’m here to help. Reach out. 🚀

Key Thinkers in the First Principles of Business Operations

In our last post, “Essential First Principles of Business Operations,” we explored the foundational principles that govern effective business operations. If you’re engaging with ChatGPT, Copilot, Gemini, or Claude about these principles, start with an instruction:

“Respond like Eric Reis, Jezz Humble, Donella H. Meadows and the best minds on the topic of Continuous Improvement. How can I build a culture of continuous improvement in my organization?”

This simple prompt will help ground the AI’s response in the insights of a proven expert, ensuring clarity, depth, and strategic thinking.

Several leading thinkers have shaped my understanding and application of these first principles through their contributions to business strategy, systems thinking, lean operations, and management. Below are some of the key thought leaders associated with each principle.

1. Value Creation

✅ The fundamental purpose of a business is to create value for customers.

  • Clayton Christensen – Developed Jobs-to-Be-Done and Disruptive Innovation, emphasizing customer needs as the foundation of value creation.
  • Peter Drucker – Stressed that the purpose of a business is to create and keep a customer.
  • Steve Jobs – Focused on breakthrough products by understanding what people truly want before they realize it.

2. Value Delivery

✅ Building an efficient and effective Value Delivery System is at the core of AgenticOps.

  • Jeff Bezos – Built Amazon around customer obsession and operational excellence.
  • Elon Musk – Applied first principles thinking to optimize logistics, supply chains, and manufacturing.
  • Taiichi Ohno – Father of Lean Manufacturing, developed the Toyota Production System.

3. Revenue Generation

✅ A business must generate revenue in proportion to the value it delivers.

  • Warren Buffett – Advocated for sustainable revenue models with strong economic moats.
  • Philip Kotler – The father of modern marketing, focusing on value-based pricing and customer-centric revenue generation.
  • Marc Andreessen – Coined “software is eating the world,” emphasizing digital-first revenue models.

4. Cost Efficiency

✅ Sustainable businesses optimize costs without compromising value.

  • Andrew Grove – Wrote High Output Management, focusing on lean cost structures and operational efficiency.
  • Jack Welch – Pioneered cost-cutting strategies and maximizing operational efficiency.
  • Sam Walton – Mastered cost efficiency in supply chains and logistics at Walmart.

5. Process Optimization

✅ All business operations are driven by processes, which should be continuously improved.

  • Edward Deming – Father of Total Quality Management (TQM), developed the PDCA (Plan-Do-Check-Act) cycle.
  • Eliyahu Goldratt – Created Theory of Constraints (TOC) to eliminate bottlenecks and optimize performance.
  • Shigeo Shingo – Pioneer of Lean & Just-in-Time manufacturing, reducing process inefficiencies.

6. Cash Flow Management

✅ Cash flow is the lifeblood of any business.

  • Benjamin Graham – Father of value investing, focused on financial discipline.
  • Ray Dalio – Developed Principles for business and financial decision-making.
  • Aswath Damodaran – Expert on valuation and cash flow-based decision-making.

7. Risk Management

✅ Every business faces operational, financial, market, and compliance risks.

  • Nassim Taleb – Developed Antifragility & Black Swan Theory, emphasizing resilience in uncertainty.
  • Jim Collins – In Great by Choice, introduced SMaC (Specific, Methodical, and Consistent) principles for risk mitigation.
  • Howard Marks – Leading thinker on financial and operational risk management.
  • Donella H. Meadows – Introduced systems thinking for risk management, focusing on feedback loops, resilience, and leverage points in complex business systems.

8. Scalability

✅ Businesses must design operations for growth.

  • Reid Hoffman – Developed Blitzscaling, focusing on hyper-growth strategies.
  • Elad Gil – Wrote High Growth Handbook on scaling businesses efficiently.
  • Eric Schmidt – Built scalable decision-making frameworks at Google.

9. People and Culture

✅ A company is only as strong as its team.

  • Simon Sinek – Developed The Golden Circle, emphasizing purpose-driven leadership.
  • Laszlo Bock – Wrote Work Rules! on high-performance work culture.
  • Patrick Lencioni – Focuses on team dynamics and leadership in The Five Dysfunctions of a Team.

10. Decision Intelligence

✅ Effective business operations rely on sound decision-making.

  • Daniel Kahneman – Developed Prospect Theory, explaining cognitive biases in decision-making.
  • Michael Porter – Created Competitive Strategy and Five Forces for structured decision-making.
  • Richard H. Thaler – Developed Nudge Theory to improve decision-making through behavioral economics.

11. Customer Focus

✅ The most successful businesses deeply understand and prioritize their customers.

  • Tony Hsieh – Built Zappos around legendary customer service.
  • Shep Hyken – Leading expert on customer experience (CX) and loyalty.
  • Don Peppers & Martha Rogers – Developed One-to-One Marketing, emphasizing deep customer relationships.

12. Continuous Improvement

✅ Adaptability and innovation drive long-term success.

  • Kaoru Ishikawa – Developed Total Quality Management (TQM) and the Ishikawa (Fishbone) Diagram for identifying inefficiencies.
  • James Clear – Wrote Atomic Habits, applying continuous improvement principles to business and personal development.
  • Eric Ries – Created The Lean Startup, emphasizing rapid iteration and learning loops.
  • Jez Humble – Co-authored Continuous Delivery, pioneering DevOps and agile software delivery methodologies.
  • Donella H. Meadows – Emphasized feedback loops and leverage points, foundational to iterative improvement and system-wide learning.

Final Thoughts: First Principles Before AI

These thought leaders and more have shaped modern business operations by applying first principles thinking, systems thinking, lean methodologies, and customer-driven models.

If you want to engage AI in deep conversations about business operations, start by grounding it in the work of these experts. Their insights continue to drive efficiency, scalability, and resilience in the world’s most successful companies.

💡 Which thought leader has influenced your approach to business the most? Let’s discuss or have your agent reach out to mine. 🚀

Essential First Principles of Business Operations

AgenticOps is the mission. Every business, regardless of current size or valuation, should have access to AI to improve its operations. Before we get to deep in this agentic AI stuff we need to take it back to basics. With all the talk about AI and the exaggerated hype about agent this and agents that, we need to remember what AgenticOps is about, improving business operations. First, we need to ground ourselves in the basics of business operations before we can benefit from AI.

As I prepare for AgenticOps, I need to move fast and think fast. I believe posts are going to come fast and heavy. My AI assistant, “George” is making the thought process a lot easier and faster to get posts out the door. Sorry for the flood, but my agents need to eat, and these words are on the diet.

So, let’s take this back to first principles. The first principles of business operations are foundational truths that govern how businesses function effectively. These principles help in building robust systems, regardless of the type of business. They aid in making informed decisions. Additionally, they optimize operations for efficiency and growth. Let’s explore some of the key first principles of business operations.

1. Value Creation

The fundamental purpose of a business is to create value for its customers. Without value creation, there is no demand, revenue, or sustainability.

  • Identify customer needs and solve real problems.
  • Deliver products/services that offer meaningful benefits.
  • Continuously improve value propositions.

2. Value Delivery

Building an efficient and effective Value Delivery System is at the core of AgenticOps.

Value must not only be created but also efficiently delivered to customers.

  • Streamline operations to reduce friction and delays.
  • Verify quality and reliability in products/services.
  • Optimize logistics, customer support, and fulfillment.

3. Revenue Generation

A business must generate revenue in proportion to the value it delivers.

  • Define a monetization strategy (pricing, sales, partnerships).
  • Align pricing with perceived and actual value.
  • Optimize revenue streams and financial health.

4. Cost Efficiency

Sustainable businesses optimize costs without compromising value.

  • Focus on reducing waste and inefficiencies.
  • Automate repetitive and manual processes.
  • Invest in technology and systems that drive efficiency.

5. Process Optimization

All business operations are driven by processes, which should be continuously improved.

  • Define, document, and refine key business processes.
  • Measure and optimize workflows to enhance productivity.
  • Use data-driven decision-making to improve performance.

6. Cash Flow Management

Cash flow is the lifeblood of any business.

  • Maintain a balance between revenue, expenses, and investments.
  • Ensure liquidity to sustain operations during downturns.
  • Forecast cash flow trends for better financial planning.

7. Risk Management

Every business faces operational, financial, market, and compliance related risks.

  • Identify, assess, and mitigate risks proactively.
  • Diversify revenue streams and operational dependencies.
  • Build resilience through contingency planning.

8. Scalability

Businesses must design operations for growth.

  • Develop systems that can handle increased demand.
  • Standardize processes and automate where possible.
  • Ensure infrastructure and human capital can scale efficiently.

9. People and Culture

A company is only as strong as its team.

  • Hire, develop, and retain top talent.
  • Foster a culture of safety, accountability, innovation, and collaboration.
  • Effectively align incentives with business goals.

10. Decision Intelligence

Effective business operations rely on sound decision-making.

  • Base decisions on data, analysis, historical experience, and first principles.
  • Implement feedback loops to refine strategies.
  • Balance short-term execution with long-term vision.

11. Customer Focus

The most successful businesses deeply understand and prioritize their customers first.

  • Gather customer feedback to drive improvements.
  • Maintain strong customer relationships and retention strategies.
  • Deliver exceptional experiences to create brand loyalty.

12. Continuous Improvement

Adaptability and innovation drive long-term success.

  • Embrace change and proactively seek better ways to operate.
  • Learn from failures and iterate rapidly.
  • Encourage a mindset of testing, learning, and optimizing.

By building business operations on these first principles, businesses can design resilient, efficient, and high-performance operations that sustain long-term success.

When you think about your business, what are its guiding principles? If you need help grounding your business operations in sound principles, reach out.

Building AI-Driven Product Teams in AgenticOps

In an AI-Driven Product environment, success is rooted in continuous improvement and guided by five core principles:

  • Clarity in communication ensures agents and operators understand what to deliver and why.
  • Strategic and tactical alignment in task execution connects high-level goals with day-to-day work.
  • Observability in performance enables continuous measurement, learning, and improvement.
  • Explainability ensures we can interpret and trust deliverables.
  • Consistency in deliverables builds client trust and enhances value of deliverables.

The journey for AI-Driven Product Teams progresses through three layers of maturity towards AgenticOps: AI-Assisted Development, Agent Development, and Agentic Delivery.

By Product Team, I mean a team that delivers a digital, data, AI, or IoT product. This product requires design and writing code.

1. AI-Assisted Development

This foundational stage focuses on training both agents and operators. The operator collaborates with their agent assistants by crafting precise prompts to direct workflows, break work items into actionable steps, and improve deliverables.

Agent Role

  • Act as specialized junior team members (e.g., marketer, developer, QA analyst, DevOps engineer, data scientist).
  • Execute prompts and produce deliverables for operator review.

Operator Role

  • Maintain control over workflows and agent task assignment, ensuring clarity in prompts and alignment with strategic and tactical goals.
  • Measure performance based on value-added time and deliverable ratings, reviews, and scores (e.g., stars, thumbs up/down, percentages).
  • Collaborate with the team to refine and solidify agent prompts, data, fine-tuning, training, workflows, policies, and templates.

Goals

  • Train operators and agents to deliver high-value deliverables with consistency and precision.
  • Build confidence in agent outputs by ensuring explainability of results and observability in performance.
  • Lay the foundation for continuous improvement through feedback and measurable progress.

Outcome
AI-Assisted Development serves as the training ground, where agents learn and improve while operators refine their ability to prompt, evaluate, and lead agents.

2. Agent Development

At this stage, agents gain more autonomy, handling complete work items while maintaining alignment with operator-defined criteria. They focus on delivering high-value deliverables efficiently and improving their ratings, reviews, and scores.

Agent Role

  • Execute work items independently, adhering to prompts and defined workflows.
  • Strive for explainability in deliverables to build operator trust.
  • Actively improve through operator feedback, targeting higher ratings, reviews, and better scores.

Operator Role

  • Shift from managing tasks to guiding agents and evaluating outcomes.
  • Monitor and analyze performance metrics (e.g., flow time, throughput, and value-added time).
  • Collaborate with the team to optimize workflows, policies, and templates.

Goals

  • Deliver predictable, high-value outputs while minimizing operator intervention.
  • Link speed to cost and value, optimizing workflows for value, efficiency, and profitability.
  • Foster a system of continuous improvement based on measurable feedback.

Outcome
Agent Development prepares agents for full autonomy by ensuring they consistently meet or exceed expectations in value, quality, and speed.

3. Agentic Delivery

In this stage, agents achieve the agentic state with full autonomy. They independently manage work items from a queue, delivering high-value deliverables aligned with strategic goals, with minimal operator oversight.

Agent Role

  • Own the entire lifecycle of a work item, from planning to execution and delivery.
  • Ensure deliverables are explainable and align with strategic and tactical objectives.
  • Continuously improve performance by adapting to feedback and refining workflows.

Operator Role

  • Define high-level goals, vision, and success criteria.
  • Monitor performance metrics and provide directional guidance only when necessary.
  • Focus on innovation and strategy while refining policies and templates to scale operations.

Goals

  • Achieve consistent, predictable, and explainable high-value deliverables.
  • Scale operations efficiently, reducing reliance on human intervention.
  • Build a self-sustaining system of Agentic Ops that continuously improves.

Outcome
Agentic Delivery transforms agents into trusted, autonomous team members capable of delivering measurable value at scale. Operators focus on strategic priorities while agents handle execution.

Continuous Improvement and Explainability

The path from AI-Assisted Development to Agentic Delivery is defined by continuous improvement and explainability. Agents are motivated to enhance their deliverables by earning higher ratings, reviews, and scores, while operators ensure clarity and alignment through refined workflows and templates.

By observing and explaining performance, operators and teams build trust in agent outputs. This system fosters a reliable, scalable process where agents evolve into autonomous contributors, consistently delivering high-value deliverables with measurable impact.

This is a lot easier said than done and there are many devils in the details, but this provides a framework to achieve Agentic Ops.

Where are you in your journey with AI Agents? I’m here if you want to talk more about taking your first step or stepping into the agentic state.

Streamlining Dependency Management: Lessons from 2015 to Today

In this throwback Tuesday post, we revamp at a dusty draft post from 2015.

In 2015, I faced a challenging problem. I had to manage dependencies across a suite of interconnected applications. It was crucial to ensure efficient, safe builds and deployments. Our system included 8 web applications, 24 web services, and 8 Windows services. This made a total of 40 pipelines for building, deploying, and testing. At the time, this felt manageable in terms of automation, but shared dependencies introduced complexity. It was critical that all applications used the same versions of internal dependencies. This was especially important because they interacted with a shared database and dependencies can change the interaction.

Back then, we used zip files for our package format and were migrating to NuGet to streamline dependency management. NuGet was built for exactly this kind of challenge. However, we needed a system to build shared dependencies once. It was necessary to ensure version consistency across all applications. The system also needed to handle local, and server builds seamlessly.

Here’s how I approached the problem in 2015 and how I’d tackle it today, leveraging more modern tools and practices.

The 2015 Solution: NuGet as a Dependency Manager

Problem Statement

We had to ensure:

  1. Shared dependencies were built once and consistently used by all applications.
  2. Dependency versions were automatically synchronized across all projects (both local and server builds).
  3. External dependencies are handled individually per application.

The core challenge was enforcing consistent dependency versions across 40 applications without excessive manual updates or creating a maintenance nightmare.

2015 Approach

  1. Migrating to NuGet for Internal Packages
    We began by treating internal dependencies as NuGet packages. Each shared dependency (e.g., ProjB, ProjC, ProjD) was packaged with a version number and stored in a NuGet repository. When a dependency changed, we built it and updated the corresponding NuGet package version.
  2. Version Synchronization
    To ensure that dependent applications used the same versions of internal packages:
    • We used nuspec files to define package dependencies.
    • NuGet commands like nuget update were incorporated into our build process. For example, if ProjD was updated, nuget update ProjD was run in projects that depended on it.
  3. Automating Local and Server Builds
    We integrated NuGet restore functionality into both local and server builds. On the server, we used Cruise Control as our CI server. We added a build target that handled dependency restoration before the build process began. Locally, Visual Studio handled this process, ensuring consistency across environments.
  4. Challenges Encountered
    • Updating dependencies manually with nuget update was error-prone and repetitive, especially for 40 applications.
    • Adding new dependencies required careful tracking to ensure all projects referenced the latest versions.
    • Changes to internal dependencies triggered cascading updates across multiple pipelines, which increased build times.
    • We won’t talk about circular dependencies.

Despite these challenges, the system worked, providing a reliable way to manage dependency versions across applications.

The Modern Solution: Solving This in 2025

Fast forward to today, and the landscape of dependency management has evolved. Tools like NuGet remain invaluable. However, modern CI/CD pipelines have transformed how we approach these challenges. Advanced dependency management techniques and containerization have also contributed to this transformation.

1. Use Modern CI/CD Tools for Dependency Management

  • Pipeline Orchestration: Platforms like GitHub Actions, Azure DevOps, or GitLab CI/CD let us build dependencies once. We can reuse artifacts across multiple pipelines. Shared dependencies can be stored in artifact repositories (e.g., Azure Artifacts, GitHub Packages) and injected dynamically into downstream pipelines.
  • Dependency Locking: Tools like NuGet’s lock file (packages.lock.json) ensure version consistency by locking dependencies to specific versions.

2. Automate Version Synchronization

  • Semantic Versioning: Internal dependencies should follow semantic versioning (e.g., 1.2.3) to track compatibility.
  • Automatic Dependency Updates: Use tools like Dependabot or Renovate to update internal dependencies across all projects. These tools can automate pull requests whenever a new version of an internal package is published.

3. Embrace Containerization

  • By containerizing applications and services, shared dependencies can be bundled into base container images. These images act as a consistent environment for all applications, reducing the need to manage dependency versions separately.

4. Leverage Centralized Package Management

  • Modern package managers like NuGet now include improved version constraints and dependency management. For example:
    • Use a shared Directory.Packages.props file to define and enforce consistent dependency versions across all projects in a repository.
    • Define private NuGet feeds for internal dependencies and configure all applications to pull from the same feed.

5. Monitor and Enforce Consistency

  • Dependency Auditing: Tools like WhiteSource or SonarQube can analyze dependency usage to ensure all projects adhere to the same versions.
  • Build Once, Deploy Everywhere: By decoupling build and deployment, you can reuse prebuilt NuGet packages in local and server builds. This ensures consistency without rebuilding dependencies unnecessarily.

Case Study: Revisiting ProjA, ProjB, ProjC, and ProjD

Let’s revisit the original example that help me figure this out in 2015 but using today’s tools.

  1. When ProjD changes:
    • A CI/CD pipeline builds the new version of ProjD and publishes it as a NuGet package to the internal feed.
    • Dependency lock files in ProjB and ProjC ensure they use the updated version.
  2. Applications automatically update:
    • Dependabot identifies the new version of ProjD and creates pull requests to update ProjB and ProjC.
    • After merging, ProjA inherits the changes through ProjB.
  3. Consistency is enforced:
    • Centralized package configuration (Directory.Packages.props) ensures that local and server builds use the same dependency versions.

The Results

By modernizing our approach:

  • Efficiency: Dependencies are built once and reused, reducing redundant builds.
  • Consistency: Dependency versions are enforced across all projects, minimizing integration issues.
  • Scalability: The system can scale to hundreds of applications without introducing maintenance overhead.

Conclusion

In 2015, we solved the problem using NuGet and MSBuild magic to enforce dependency consistency. Today, with modern tools and practices, the process is faster, more reliable, and scalable. Dependency management is no longer a bottleneck; it’s an enabler of agility and operational excellence.

Are you ready to future-proof your dependency management? Let’s talk about optimizing your build and deployment pipelines today.

AgenticOps: Transform Your Business with Agent Enhanced Teams

Welcome to 2025 and the year of AgenticOps.

Can your business operating systems think for themselves? Can they adapt in real time? Do they make decisions that perfectly align with your strategic goals? Imagine a system that is seamlessly integrated with your workflows. It not only reduces manual overhead but actively amplifies your team’s effectiveness. This vision is no longer aspirational—it’s here. Welcome to AgenticOps, the new frontier in how organizations operate, collaborate, and deliver value.

What is AgenticOps?

AgenticOps (Agentic Operations) isn’t your typical automation or AI solution. It’s a comprehensive, structured framework where autonomous agents collaborate with human operators to achieve shared strategic and tactical goals. Picture a network of hyper-specialized agents. These could include planner agents, research agents, developer agents, and more. They work tirelessly behind the scenes to streamline processes. They optimize results and keep priorities aligned.

These agents are not mere tools. They are intelligent systems designed to learn, adapt, and execute tasks as an extension of your team. These systems enable better alignment and faster decisions. They also promote proactive problem-solving and seamless orchestration of complex workflows.

The Core Principles of AgenticOps

AgenticOps is built on three foundational pillars:

1. Autonomy

Agents operate independently, performing tasks without constant human oversight. But, they rely on human feedback and approval for critical decision points. These agents autonomously monitor, analyze, and act on data, ensuring their actions align with your organization’s strategic objectives.

2. Collaboration

Agents are interconnected, working as a cohesive system. They share insights, offer feedback, and coordinate activities across workflows. This eliminates silos, streamlines communication, and ensures seamless collaboration between agents and human operators.

3. Accountability

Every agent’s action is transparent, traceable, and purpose-driven, aligning with predefined objectives. Accountability fosters trust and ensures operational integrity, empowering teams to rely on agents while maintaining control.

The Value of AgenticOps

The transformative potential of AgenticOps lies in the value it delivers to organizations:

1. Strategic Alignment

By embedding strategic goals into every operation, AgenticOps ensures resources are directed toward the most impactful tasks. This integration drives measurable business outcomes.

2. Efficiency at Scale

Agents handle repetitive, high-volume tasks with precision, freeing human operators to focus on creative, innovative, and high-priority initiatives.

3. Real-Time Adaptability

In dynamic environments, agents adapt instantly—reallocating resources, recalibrating priorities, and maintaining operational continuity in response to market demands.

4. Enhanced Visibility

Agents continuously monitor and report on operational performance, providing unparalleled insights into bottlenecks, inefficiencies, and opportunities for improvement.

The Specialized Roles of Agents in AgenticOps

AgenticOps systems are powered by specialized agents designed to handle distinct responsibilities:

  • Strategy Agents: Define and maintain the strategic objectives that guide all operations.
  • Plan Agents: Develop and update plans, ensuring timelines and milestones align with goals.
  • Research Agents: Conduct user research, market analysis, and feasibility studies to provide actionable insights.
  • Tech Agents: Manage the heavy lifting in development, design, QA, and release engineering.
  • Manager Agents: Oversee workflows, align tasks with strategic goals, and maintain accountability.

Together, these agents form a synergistic system that empowers businesses to operate with unprecedented precision and agility.

A Real-World Example of AgenticOps in Action

Consider a product team managing a portfolio of client projects. In traditional setups, tracking progress, aligning with strategic goals, and adjusting priorities require multiple tools, meetings, and manual interventions.

With AgenticOps, Manager Agents dynamically analyze work in progress, monitor KPIs, and provide actionable insights. If a bottleneck arises, agents flag the issue, recommend solutions, and execute corrective actions autonomously. This proactive approach keeps revenue targets on track, eliminates delays, and ensures client satisfaction.

How to Adopt AgenticOps

Transitioning to AgenticOps requires a strategic, phased approach:

  1. Start Small: Identify high-impact areas where agents can deliver immediate value, such as planning or research.
  2. Integrate Incrementally: Introduce agents gradually, ensuring they complement existing workflows.
  3. Empower Teams: Provide teams with the training and tools needed to collaborate effectively with agents.
  4. Measure Success: Use metrics to track the impact of agents, iterating to refine their contributions.

The Future of Work is AgenticOps

AgenticOps is more than a technological advancement. It’s a thought process and paradigm shift that will force businesses to evolve. Otherwise, they lose against businesses that make the shift. Its not a new idea, its our framework to take advantage of the rapid advancements in AI. By embedding intelligence into operations, businesses become adaptive, resilient, and capable of thriving in fast-paced environments.

This isn’t about replacing humans. It’s about empowering them—reducing cognitive load and enabling them to focus on what truly matters. The question is no longer, “What can your team do for your business?” It’s now, “What can your agent-augmented team achieve for your business?”

Have you begun the shit to AI driven operations? How are you succeeding with something like AgenticOps? Let us know your thoughts and join the conversation.

2025: The Year AI Transforms Work and Industry at Scale

Happy New Year! Here are my bet’s for 2025.

The rapid evolution of artificial intelligence is reshaping industries and redefining how we work. In 2025, I predict that several transformative trends will reshape the landscape of software development, industry specialization, and workforce dynamics. Below, we explore these predictions, provide additional insights, and examine the opportunities and risks that come with these changes.

1. Accelerating Improvements in LLMs

Large Language Models (LLMs) will continue to push the boundaries of what AI can do. With advancements in transformer, SSM, and other architectures, fine-tuning, and multimodal learning, LLMs will deliver faster, more accurate, and contextually rich results. We may even see an new architecture that pushes LLMs ahead even faster.

Opportunities

  • Enhanced productivity and creativity in tasks like content creation, research, and customer interaction.
  • Multimodal capabilities enabling seamless integration of text, images, and even video into workflows.

Challenges

  • Ethical concerns around misuse, bias, and misinformation.
  • Increased compute requirements may exacerbate energy consumption concerns.

2. Smaller Models, Big Potential

Compact LLMs leveraging distillation, pruning, and quantization will achieve near-parity with today’s largest models. This shift will democratize AI, making it accessible for edge devices, IoT applications, and industries with limited compute resources.

Opportunities

  • Cost-effective AI solutions for SMBs.
  • Expanding AI’s footprint into rural and underserved areas via low-power devices.

Challenges

  • Balancing efficiency with accuracy in critical applications like healthcare diagnostics or autonomous vehicles.

3. Industry-Specific AI Takes Center Stage

The rise of verticalized AI solutions tailored to specific industries, such as marketing, healthcare, finance, and legal, will dominate the market. These solutions will provide unparalleled domain expertise, driving faster ROI for businesses.

Opportunities

  • Precision and relevance in solving domain-specific challenges.
  • Increased trust in AI adoption as models demonstrate real-world impact.

Challenges

  • Heavy reliance on proprietary data could lead to monopolistic behavior or widen the gap between industry leaders and smaller players.

4. AI-Driven Development as the Norm

AI coding assistants like GitHub Copilot, Cursor, and Aider are already changing the way developers work. In 2025, AI will be fully integrated into development ecosystems, assisting with ideation, debugging, and even deployment.

Opportunities

  • Streamlined development cycles, reducing time to market.
  • Better accessibility for non-traditional developers, diversifying the talent pool.

Challenges

  • Over-reliance on AI could erode foundational coding skills.
  • Potential loss of creativity in problem-solving as AI-driven patterns become standardized.

5. Coding Becomes a Commodity

As AI handles the technical details of coding, the value of knowing how to code will diminish. Instead, the ability to guide AI assistants, define clear objectives, and solve high-level problems with code will become paramount. The experience to know when code is good or bad becomes more important than the just the ability to code.

Opportunities

  • Broader inclusion of non-technical professionals in tech projects.
  • Emergence of new roles focused on prompt engineering, strategy, and oversight.

Challenges

  • A potential skills gap for current developers who fail to adapt.
  • Risk of job displacement without sufficient upskilling initiatives.

6. Shippable AI-Driven Products with Minimal Oversight

In 2025, performant coding agents capable of turning product requirements into deployable solutions will emerge. These agents will handle well-defined scopes but may still require human oversight for complex or high-stakes applications.

Opportunities

  • Faster MVP development for startups.
  • Automated maintenance of legacy systems, freeing up human resources for innovation.

Challenges

  • Ensuring quality control and accountability in AI-generated products.
  • Difficulty in generalizing complex, nuanced requirements.

7. OpenAI Dominates but Faces Competition

OpenAI will maintain a stronghold on the market, but competition from Google, Meta, Anthropic, Cohere, and open-source ecosystems will heat up.

Opportunities

  • Diverse options for businesses to choose from, fostering innovation and reducing costs.
  • Strengthening open-source movements that promote transparency and collaboration.

Challenges

  • Risk of market fragmentation, making it harder for businesses to standardize solutions.
  • Proprietary dominance could limit interoperability.

8. AI Agencies Replace Traditional Agencies

Small AI agencies will rise to prominence, offering specialized services in automation, data modeling, and AI-driven marketing and development. These agencies will cater to SMBs, replacing traditional creative and technical firms.

Opportunities

  • Affordable, tailored AI solutions for local markets.
  • Innovation in personalized customer experiences and hyper-local strategies.

Challenges

  • Ethical dilemmas in hyper-targeted advertising.
  • Limited oversight in emerging markets where regulation lags behind.

9. Data Becomes the New Gold for Real This Time

Businesses will finally fully realize the value of their proprietary data, using it to train domain-specific models. Data-rich companies will dominate their industries by leveraging AI in unique and powerful ways.

Opportunities

  • Competitive differentiation through unique datasets.
  • Greater investment in data quality, security, and governance.

Challenges

  • Risk of data monopolies exacerbating inequality.
  • Increased cybersecurity threats targeting proprietary datasets.

Navigating Risks and Building for the Future

While the predictions for 2025 are exciting, they also come with challenges that require proactive measures:

  • Upskilling the Workforce: Governments, businesses, and educational institutions must collaborate to prepare the workforce for AI-driven roles.
  • Regulating Ethically: Establishing global standards for AI use will be crucial to avoid misuse and ensure equitable benefits.
  • Driving Sustainability: Advancements in AI must prioritize energy efficiency and sustainable practices.

As we move into 2025, businesses that embrace these changes while navigating risks will unlock unprecedented opportunities. The future of work and industry is brighter, more efficient, and deeply collaborative—driven by AI.

Are you ready to harness the power of AI for your business? Let’s talk about it and get your business ready for our AI future.

Writing Automated Integration Tests by the Numbers

In this Throwback Tuesday post is a revamped draft post from January 2014 where I wrote about writing SpecFlow tests. Here I am generalizing the processes because I don’t use SpecFlow anymore.

One thing I learned in the Marine Corps was to do things by the numbers. It was a natural fit for my analytical mind. Plus, let’s face it, we were told we were useless maggots as dumb as a rock, and this training method was apparently the easiest way to teach a bunch of recruits. Naturally, it worked great for a dumb rock like me, OORAH!

Because of this lesson, I’ve always tried to distill common processes into neat little numbered lists. They’re easy to refer to, teach from, and optimize. When I find a pattern that works across a wide range of scenarios, I know I’ve hit on something useful. So, with that in mind, here’s how I approach writing automated integration tests by the numbers.

1. Understand the Test Data Needs

The first step in any integration test is figuring out the test data you need. This means asking questions like, “What inputs are required? What outputs am I validating?” You can’t test a system without meaningful data, so this step is non-negotiable.

2. Prepare the Test Data

Once you know what you need, it’s time to create or acquire that data. Maybe you generate it on the fly using a tool like Faker. Maybe you’ve got pre-existing seed scripts to load it. Whatever the method, getting the right data in place is critical to setting the stage for your tests.

3. Set Up the Environment

Integration tests usually need a controlled environment. This might involve spinning up Docker containers, running a seed script, or setting up mock services. Automating this step wherever possible is the key to saving time and avoiding headaches.

4. Run a Manual Sanity Check

Before diving into automation, I like to run the test manually. This gives me a feel for what the system is doing and helps catch any obvious issues before I start coding. If something’s off, it’s better to catch it here than waste time troubleshooting broken automation.

5. Create Reusable Test Components

If the test interacts with a UI, this is where I’d create or update page objects. For APIs or other layers, I’d build out reusable components to handle the interactions. Modular components make tests easier to write, maintain, and debug.

6. Write and Organize the Tests

This is the core of the process: writing the test steps and organizing them logically. Whether you’re using SpecFlow, pytest, or any other framework, the principle is the same: break your tests into clear, reusable steps.

7. Tag and Manage Tests

In SpecFlow, I used to tag scenarios with @Incomplete while they were still under development. Modern frameworks let you tag or group tests to control when and how they run. This is handy for managing incomplete tests or running only high-priority ones in CI/CD pipelines.

8. Debug and Refine

Once the test is written, run it and fix any issues. Debugging is a given, but this is also a chance to refine your steps or improve your reusable components. The goal is to make each test rock-solid and maintainable.

Lessons Learned

Breaking things down by the numbers isn’t just about being organized—it’s about being aware of where the bottlenecks are. For me, steps 1 and 2 (understanding and preparing test data) are often the slowest. Knowing that helps me focus on building tools and processes to speed up those steps.

This approach also makes training others easier. If I need to onboard someone to integration testing:

  1. Pair with them on a computer.
  2. Pull out the “Integration Tests by the Numbers” list.
  3. Call them a worthless maggot as dumb as a rock (just kidding… mostly).
  4. Walk through the process step by step.

Relevance Today

Even though I don’t use SpecFlow anymore, this process still applies. Integration testing frameworks and tools have evolved, but the principles are timeless. Whether you’re using Playwright, Cypress, or RestAssured, these steps form the foundation of effective testing.

What’s different now is the tooling. Tools like Docker, Terraform, and CI/CD pipelines have made environment setup easier. Test data can be generated on the fly with libraries like Faker or FactoryBot. Tests can be grouped and executed conditionally with advanced tagging systems.

The key takeaway? Processes evolve, but the mindset of breaking things down by the numbers is as valuable as ever. It’s how I keep my integration tests efficient, maintainable, and scalable.

Maximize Business Efficiency with SAS Agent

I am spending a lot of time focusing on how to bring AI to bear on business challenges. One area that intrigued me lately is strategic alignment. Getting everyone on the same page is challenging. I put some thought into how an AI agent can help.

Things are moving so fast, and business priorities are constantly shifting. So, staying strategically aligned is much harder today than it was in 1989 when I started my first business. To move at this accelerated pace and stay aligned, we developed an AI agent named the Strategic Alignment Scoring Agent. This agent is guided by the Strategic Alignment Score (SAS), formalizing strategic prioritization into a simple scoring algorithm. This AI-driven tool ensures that every decision counts. It aligns work items with our strategy. It also aligns with the strategy of our client’s. Here’s how it transforms task management and decision-making for our agency.

The Challenge: Managing Priorities in a Fast-Paced World

Every business leader knows the struggle: a growing list of tasks and limited resources. Determining what deserves attention can feel like navigating a maze. Without clear prioritization, teams risk wasting time on low-impact tasks while critical initiatives fall behind.

The Vision: Data-Driven Strategic Alignment

Imagine a world where every task supports your company’s strategic goals. With a SAS Agent, that vision becomes reality. This agent examines work items using SAS’s robust analytics platform. It employs a scoring algorithm to assign a strategic alignment score to each work item. The result? A clear, prioritized task list that maximizes value delivered and impact.

How the SAS Agent Works

  1. Input: Upload your list of work items in common formats like Excel or CSV and give your SAS and weights. This can also be automated through an API.
  2. Analysis: The SAS Agent uses proprietary SAS analytics and scoring algorithms. The agent evaluates tasks based on their alignment with strategy prioritizes them to best achieve desired outcomes.
  3. Prioritization: Tasks are scored and ranked, giving you an actionable, ordered list.
  4. Integration: Ability to seamlessly integrate with popular project management platforms ensures smooth workflow integration.

Why It Matters: Delivering Real Business Value

  • Increased Efficiency: Spend less time debating priorities and more time delivering results.
  • Enhanced Transparency: Clear, data-backed rankings reduce ambiguity and foster accountability.
  • Better Decision-Making: Leadership teams can allocate resources with confidence, knowing each task supports the broader corporate mission.

How SAS Is Calculated

We calculate the SAS by evaluating key business factors like strategic alignment, value delivered, feasibility, and urgency. Each factor is assigned a weight reflecting its strategic importance. Work items are scored based on these components, ensuring top-priority tasks are clearly identified and ranked for action.

The SAS Agent adjusts weights at the strategic level. This dynamic adaptation helps the agent meet changing business needs. It allows us to focus on what matters most. The AI-powered agent continuously evaluates work items. It can suggest adjusting weights. This ensures work delivery stays aligned with evolving strategic goals.

Similar Frameworks and How SAS Compares

The Strategic Alignment Score (SAS) is more than a prioritization framework. It’s a customized decision-making system built to optimize strategic impact. It isn’t new or groundbreaking. It borrows and adapts best practices from various prioritization and decision-making models and packaged them to fit our business context. Here’s how SAS compares to well-known frameworks:

  1. Weighted Scoring Model
    • Similarity: SAS shares the concept of assigning weighted scores across multiple criteria.
    • Difference: SAS explicitly incorporates business strategy dimensions. These include strategic alignment, feasibility, and viability. These dimensions are broader than typical cost-benefit or ROI-focused models.
  2. MoSCoW Prioritization (Must have, Should have, Could have, Won’t have)
    • Similarity: Both focus on prioritizing tasks based on impact and necessity. This was a major feature of our earlier prioritization system.
    • Difference: MoSCoW is more categorical, while SAS provides a granular, data-driven scoring mechanism.
  3. Eisenhower Matrix (Urgent vs. Important)
    • Similarity: SAS considers urgency and impact (value delivered), aligning with the matrix’s core dimensions.
    • Difference: SAS expands with extra metrics like resource availability, complexity, and feasibility, making it more comprehensive.
  4. RICE Scoring (Reach, Impact, Confidence, Effort)
    • Similarity: Both frameworks quantify impact and feasibility while considering constraints.
    • Difference: SAS covers strategic and operational dimensions, while RICE is more product-focused.
  5. OKR Framework (Objectives and Key Results)
    • Similarity: Both are designed to align tasks with strategic goals.
    • Difference: OKRs define goals and track results at a high level. SAS scores and prioritizes individual work items based on detailed, weighted criteria.
  6. SAFe Weighted Shortest Job First (WSJF)
    • Similarity: WSJF uses a similar scoring approach, prioritizing tasks based on economic impact and urgency.
    • Difference: SAS has a broader application that extends beyond overly complex Agile SAFe environments. It emphasizes alignment with both agency and client objectives.

Real-World Use Cases

  • Project Managers: Prioritize tasks within complex projects to ensure critical goals are met first.
  • Analysts: Evaluate quarterly tasks against strategic targets for data-driven reporting.
  • Executives: Guide top-level planning and budget decisions using clear task prioritization.

Potential Challenges and Mitigations

While the SAS Agent offers clear benefits, it’s important to recognize potential challenges that arise when implementing such a system:

  • Subjectivity in Weight Assignment: Assigning weights to different business factors can introduce subjectivity and bias.
    • Mitigation: Establish clear scoring guidelines and involve cross-functional teams in setting weights to ensure balanced and consistent evaluations.
  • Data Accuracy and Completeness: Incomplete or inaccurate data entry can compromise the reliability of scores.
      • Mitigation: Implement data validation protocols and require standardized data formats for input submissions.
    • Over-Reliance on Automation: Teams become overly dependent on automated scoring, overlooking qualitative business insights.
        • Mitigation: Use SAS as a decision-support tool. It should not replace decision-making but support it. Incorporate expert reviews for critical tasks. Overrule the agent when necessary.
      • Complexity in Setup and Maintenance: First setup and ongoing maintenance of a SAS Agent need significant effort.
          • Mitigation: Start with a simplified version of a SAS Agent and expand over time, ensuring proper training and documentation.

        Organizations that want to leverage something like a SAS Agent must tackle these potential challenges proactively. This enhances strategic alignment and optimizes task prioritization.

        Looking Ahead

        The SAS Agent isn’t just a tool, it’s helps us build a mindset that moves continuous improvement to strategic alignment. We constantly review our performance. We adjust our scoring weights during retrospectives. This approach ensures that our work remains in sync with evolving client and agency needs. As we grow, SAS will stay a cornerstone of our operational strategy, guiding us toward sustained success.

        Ready to elevate your strategy alignment game? Implementing a tool like a SAS Agent can transform how you prioritize, execute, and succeed. What could your team achieve with smarter priorities?