Category: AI

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.

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.

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?

        A Future Vision of Software Development

        From Coders to System Operators

        As artificial intelligence (AI) continues reshaping industries, the role of software development is undergoing a profound transformation. Writing code is becoming less about crafting individual lines of code and more about designing systems of services that deliver business value. Development is shifting from writing code to creative problem-solving and systematic orchestration of interconnected services.

        The End of Coding as We Know It

        Code generation has become increasingly automated. Modern AI tools can write boilerplate code, generate tests, and even create entire applications from high-level specifications. As this trend accelerates, human developers will move beyond writing routine code to defining the architecture and interactions of complex systems and services.

        Rather than focusing on syntax or implementation details, the next generation of developers will manage systems holistically, designing services, orchestrating workflows, and ensuring that all components deliver measurable and scalable user, client, and business value.

        The Rise of the System Operator

        In this emerging paradigm, the role of the System Operator comes into focus. A System Operator oversees a network of AI-driven assistants and specialized agents, ensuring the system delivers maximum value through continuous refinement and coordination.

        Key Responsibilities of the System Operator:

        1. Define Value Streams: Identify business goals, define value metrics, and ensure the system workflow aligns with strategic objectives.
        2. Design System Architectures: Structure interconnected services that collaborate to provide seamless functionality.
        3. Manage AI Agents: Lead AI-powered assistants specializing in tasks like strategy, planning, research, design, development, marketing, hosting, and client support.
        4. Optimize System Operations: Continuously monitor and adjust services for efficiency, reliability, and scalability.
        5. Deliver Business Outcomes: Ensure that every aspect of the system contributes directly to business success.

        AI-Augmented Teams: A New Kind of Collaboration

        Traditional product development teams will evolve into AI-Augmented Teams, where every team member works alongside AI-driven agents. These agents will handle specialized tasks such as market analysis, system design, and performance optimization. The System Operator will orchestrate the work of these agents to create a seamless, value-driven product development process.

        Core Roles in an AI-Augmented Team:

        • Strategist: Guides the product’s vision and sets business goals.
        • Planner: Manages delivery timelines, budgets, and project milestones.
        • Researcher & Analyst: Conducts in-depth user, customer, market, technical, and competitive analyses.
        • Architect & Designer: Defines system architecture and creates intuitive user interfaces.
        • Developer & DevOps Tech: Implements features and ensures smooth deployment pipelines.
        • Marketer & Client Success Tech: Drives user adoption, engagement, and retention.
        • Billing & Hosting Tech: Manages infrastructure, costs, and financial tracking.

        System Operator: A New Job Description

        A System Operator is like an Uber driver for business systems. Product development becomes a part of the gig economy.

        Operators need expertise in one or more of the system roles with agents augmenting their experience gaps in other roles. System Operators can be independent contractors or salaried employees.

        Title: System Operator – AI-Augmented Development Team

        Objective: To manage and orchestrate AI-powered agents, ensuring the seamless delivery of software systems and services that maximize business value.

        Responsibilities:

        • Collaborate with other system operators and AI-driven assistants to systematically deliver and maintain system services.
        • Define work item scope, schedule, budget, and value-driven metrics.
        • Oversee service performance, ensuring adaptability, scalability, and reliability.
        • Lead AI assistants in tasks such as data analysis, technical research, and design creation.
        • Ensure alignment with client and agency objectives through continuous feedback and system improvements.

        Skills and Qualifications:

        • Expertise in system architecture and service-oriented strategy, planning, and design.
        • Strong understanding of AI tools, agents, and automation frameworks.
        • Ability to manage cross-functional teams, both human and AI-powered.
        • Analytical mindset with a focus on continuous system optimization.

        Conclusion: Embracing the Future of Development

        The role of developers is rapidly evolving into something much broader, more strategic, and less focused on boilerplate coding. System Operators will lead the charge, leveraging AI-powered agents to transform ideas into scalable, value-driven solutions. As we move toward this new reality, development teams must embrace the change, shifting from code writers to orchestrators of complex service ecosystems that redefine what it means to build software in the AI era.

        It’s Been a Long Time

        It’s been so long since I wrote anything up here or even felt the desire to write. I’m woke, not the political connotation, but the I’m woke to AI meaning of woke. I wanted to start sharing my experiences again, but does it matter? AI can write this faster and better, but AI can’t have my experience unless I give it my experience. So, here’s my experience.

        I created my first OpenAI GPT and Personal Assistant. I also looked into integrating them with AutoGen. The excitement and the fear in me was a visceral experience. On one hand, these things can do some real damage by some with good or bad intentions. On the other hand, so could the invention of the gun or even electrical utilities, danger is a part of the human existence but it doesn’t stop our invention or evolution.

        On one foot, these things are awesome! On the other foot, I said that about CQRS, microservices, Kubernetes, the simplest things can evoke emotion from a human or feel like another failed attempt to evoke emotion or action. I guess that’s why story telling is such a great skill to have. Triggering emotion, good or bad, is the pathway to getting someone’s attention, desire, action, engagement, commitment…, but I digress. We’ll talk about Storyboards later.

        Anyway, here’s my first agent.

        https://chat.openai.com/g/g-gs7BsbKPZ-the-product-architect-s-assistant

        The Product Architect’s Assistant

        I’m a Senior Digital, Data, and IoT Product Architect ready to assist with problem discovery, requirements analysis, solution vision and story, system design and diagraming, and resource specifications.

        I actually enjoyed talking to my assistant. Now to work with it on how I want to do problem discovery, requirements analysis, solution vision and story, system design and diagraming, and resource specifications. I have a feeling this is going to be awesome.

        I wonder what else I can teach it to do. I have a desired to name it, like its my child. This is insane.

        Now to the real reason I’m here. I am trying to search for my agent, but I can’t find it or many other agents for that matter. The link to the agent works but it doesn’t appear to be indexed. I was hoping that putting up a page with the link will help seed the URL in the search index.

        How am I searching for my URL? Glad you asked. I am using a Google Search site operator.

        site:https://chat.openai.com/g/g-gs7BsbKPZ-the-product-architect-s-assistant

        Google says, “If a URL is indexed in Google, it can show up in search results for site: queries that are related to the URL, however it’s not guaranteed.”

        Google Search Central

        The “it’s not guaranteed” part had me worried. I’ve seen this operator, but never used it or even played with it. So, let’s play.

        • The “site:” search operator on Google Search is used to show results from a specific domain, URL, or URL prefix.
        • It is helpful for site owners to check which of their pages are indexed, understand how specific URLs are indexed for certain terms, and identify spam issues.
        • The list of URLs returned by this operator is not exhaustive, especially for larger sites, and more specific prefixes in the query may yield better results.
        • While it can show indexed URLs under a specified prefix, it does not guarantee the inclusion of all indexed URLs.
        • The operator does not rank results when used without a query term; it typically shows the shortest URL at the top with other results appearing in a somewhat random order.

        So, that’s not going to work, but it’s very interesting. Let’s dig a little more.

        I wondered if my agent’s page is even in the Google search index. The page doesn’t seem to block robots

        <meta name="robots" content="index, follow">
        • index: The crawler is allowed to include this page in search engine results.
        • follow: The crawler can follow the links on this page, potentially indexing those linked pages as well.

        Maybe I can refine by operator search somehow. Here are some other Google search operators I found that can improve my search: 

        • Intext: Returns links to websites that contain the search term in blocks of text
        • Allintext: Returns links to websites that contain all specified keywords in the body of the website
        • Intitle: Returns web pages that contain a certain term or terms in the title
        • Allinurl: Returns pages that contain the search query specified in the URL
        • Inanchor: Locates specific keywords within anchor text

        I’ll start with title, “ChatGPT – The Product Architect’s Assistant.”

        site:https://chat.openai.com/g/g- intitle:ChatGPT – The Product Architect’s Assistant

        I shortened the site prefix to include all pages indexed at this prefix.

        Nope, nothin’, nada. I give up for now. Let’s see if this gets any play, I probably don’t have anymore SEO juice on this blog, but I need to try to prove the hypothesis is wrong.

        Anyway, hope you check out my agent. Its just another wrapper around ChatGPT, but I am planning on teaching it some new tricks very soon. Let me know if you have an agent or would like to see this one do a new trick for you (within context and reason… of course).

        Happy Making!