Tagged: AgenticOps

Execution is Everything: Building an AgenticOps Playbook That Works

Ideas are easy; execution is the hard part.

We’ve all seen great strategies gather dust simply because the path from planning to action wasn’t clear. The problem isn’t always the ideas or the people, often, it’s the absence of a structured playbook for execution.

When execution falters, it’s usually due to unclear roles, inconsistent processes, or poor communication. Over the years, I’ve seen firsthand how these issues erode momentum and hinder even the most talented teams.

A practical playbook addresses these pitfalls directly. It documents not just what needs to be done, but also how to do it consistently, who is responsible at each step, and why it matters. Clear processes remove guesswork, improve collaboration, and make execution repeatable and scalable.

But a good playbook isn’t rigid. It’s a living document, evolving as teams learn and conditions change. Regularly scheduled feedback loops ensure continuous improvement, allowing the team to adapt swiftly and effectively.

Recently, I’ve been exploring the idea of “Playbooks as Code,” inspired by the concept of infrastructure as code. Infrastructure as code allows teams to provision and manage cloud resources through scripts, ensuring consistency, measurability, and testability. Similarly, implementing playbooks as automated workflows, using tools like Microsoft Power Automate or Zapier, lets us codify execution steps. This approach transforms a documented playbook into a deployable, executable workflow, initiated at the push of a button. It ensures consistent, measurable, and testable workflows, significantly enhancing reliability and efficiency.

If you’re finding your team struggles to turn strategic intent into results, consider whether your execution clarity matches your strategic clarity. Building a detailed, flexible execution playbook, and perhaps exploring playbooks as code, might just be the most impactful thing you do this year.

What’s been your experience with execution playbooks or automated workflows? I’d love to learn from your insights. If you want to build one with me, let’s talk about it.

Aligning Client Goals with User Needs: It’s Not Either-Or

The balancing act between what clients (product owners) want and what users need isn’t easy, but it doesn’t have to be a trade-off. Often, teams feel torn prioritizing client objectives for quick wins or leaning heavily into user needs for long-term satisfaction. But true strategic clarity comes from aligning these perspectives, not choosing between them.

Think about it, clients seek measurable outcomes, whether it’s revenue, market share, or operational efficiency. Users, meanwhile, value intuitive experiences that genuinely solve their problems. Misalignment can lead to products that look good on paper but fail in practice.

In retrospect, I’ve learned through experience that the secret lies in embedding user-centric design into strategic planning from day one. When users’ needs directly inform business objectives, something powerful happens, products resonate deeply, adoption grows, and client goals naturally follow.

This isn’t theoretical, it’s practical wisdom. By clearly documenting how each feature, action, or decision maps to both client objectives and user needs, ambiguity fades. Teams make better decisions faster because they have a north star guiding every step.

Ultimately, strategic clarity isn’t about compromising or pleasing everyone superficially. It’s about achieving alignment that creates genuine, sustainable value for all stakeholders involved.

Applying this concept to AgenticOps is critical, especially given widespread uncertainties around the value, safety, and trustworthiness of AI among clients and users. Establishing clear, transparent strategies early in the process can significantly influence the success or failure of an AgenticOps implementation. 

What’s your approach to balancing these needs? I’d love to hear your thoughts, let’s talk about it.

AgenticOps: From Strategy to Continuous Improvement

Have you ever had a great idea fall flat during execution? Or found your team stuck between prioritizing client demands and user needs? Perhaps you’ve struggled with chaos in data management or wondered how to effectively measure and improve performance. These challenges aren’t unique I’ve encountered and wrestled with them too.

That’s why I’m writing a series on AgenticOps as a collection of insights and experiences aimed at navigating the complex world of product strategy, execution, technical workflow planning, disruptive marketing, and continuous improvement.

Throughout this series, we’ll explore AgenticOps and:

  • Strategic clarity and how aligning client goals and user needs can drive powerful outcomes.
  • Using execution playbooks to turn great strategies into actionable and consistent results through clear roles, processes, and “playbooks as code.”
  • Intelligent workflow architecture and managing data complexity with adaptive, AI-driven workflows.
  • Disruptive go-to-market strategies is interesting because AI is going to disrupt more than markets. I think bold disruption is essential for impactful market entries for AI first companies and reentry for incumbents retooling with AI.
  • Continuous improvement systems as robust measurement systems that drive ongoing growth and improvement.

My goal with this series is not only to share what I’ve learned but also to start meaningful conversations. As I wrap my head around how to apply AI to business problems for clients in my day job, this is how I record my thoughts. If you are thinking about similar topics, I invite you to read, reflect, and share your experiences and insights along the way.

Stay tuned for the upcoming posts, and feel free to jump into the discussion at any time! I talk to AI too much, so I could use some human interaction.

AgenticOps Optimization with Graded Feedback Loops

To optimize our AgenticOps workflow, we need a structured grading system that evaluates each agent’s output. These scores will drive continuous improvement, refining both workflow logic and AI models.

1️⃣ First Principles: Why Grade Agent Outputs?

  1. Measure Effectiveness – Quantify the performance of automated actions.
  2. Improve Decision-Making – Identify patterns in approved vs. rejected outputs.
  3. Fine-Tune AI Agents – Adjust response generation models based on feedback.
  4. Reduce Human Intervention – Increase automation where confidence is high.

2️⃣ Agent Performance Grading System

Each agent’s output can be graded based on predefined evaluation criteria.

2.1 Defining the Grading Criteria

For each AgenticOps step, we define a scoring model (0-100) based on key metrics:

Example:

  • If an AI-generated reply is rejected, log why (e.g., “Too formal,” “Missing details”).
  • If a categorization error occurs, adjust classification model weights.

3️⃣ Implementation in Power Automate

Step 1: Store Grading Data

  • Each agent’s output is scored after human review.
  • Store feedback in database, Azure Blob, Dataverse, SharePoint, or SQL DB.

Step 2: Automate Feedback Processing

  • If an agent scores below a threshold, flag for model retraining.
  • If an agent performs well consistently, increase automation confidence.

Step 3: Adjust AI Models Dynamically

  • Use Azure OpenAI fine-tuning for response agents.
  • Use reinforcement learning for decision-making agents.
  • Optimize categorization AI models with feedback.

Step 4: Power BI Dashboard for Analytics

  • Track agent performance over time.
  • Identify patterns in rejections and bottlenecks.
  • Provide insights for workflow tuning.

4️⃣ Adaptive Learning & Continuous Improvement

How The System Evolves

  1. Each agent’s performance is logged.
  2. Feedback is analyzed in real-time.
  3. Underperforming models are flagged for updates.
  4. Over time, AI agents improve their accuracy.
  5. Manual review workload decreases as automation confidence grows.

Scaling This System

  • Introduce self-adjusting automation thresholds based on past performance.
  • Train AI to predict when human review is necessary.
  • Implement continuous learning pipelines for AI model updates.

5️⃣ What’s Next?

  • Where should we log agent grades? (database, Azure Blob, Dataverse, or SharePoint?)
  • How frequently should we retrain AI models? (Weekly, Monthly?)
  • Do you want Power BI dashboards to track agent performance trends?

This graded feedback system will ensure that AgenticOps evolves into a highly optimized, self-improving workflow. I’ll grade your agents if you grade mine! 🚀

Enhancing AgenticOps with Observability

To ensure an AgenticOps system remains efficient, explainable, and continuously improving, we need Agent Observability as a core feature. This enables monitoring, debugging, and optimizing agent workflows just as we would in a human-managed system.

1️⃣ First Principles of Agent Observability

Agent observability allows us to:

  1. Track Agent Behavior – Log all actions and decisions for auditing.
  2. Measure Agent Performance – Grade outputs, detect failures, and identify optimization areas.
  3. Explain Agent Decisions – Ensure transparency in AI-generated actions.
  4. Detect and Resolve Bottlenecks – Identify slowdowns and inefficiencies in workflows.
  5. Enable Continuous Learning – Use real-world feedback to refine models.

2️⃣ Key Observability Components

To implement observability, we need four core layers:

2.1 Logging & Traceability

  • What: Log all agent actions, inputs, outputs, and decision paths.
  • How: Store structured logs in Database, Azure Blobs, Dataverse, or SharePoint.
  • Why: Enables debugging and root cause analysis.

Example:

  • An agent categorizes an email incorrectly → Logs capture model confidence score, decision rationale, and correction applied.

2.2 Monitoring & Alerts

  • What: Real-time monitoring of agent activity, errors, and response times.
  • How: Use Power Automate monitoring, Application Insights (Azure), or Power BI dashboards.
  • Why: Detect failures or anomalies in agent workflows.

Example:

  • If an agent’s response generation time exceeds a threshold, trigger an alert for investigation.

2.3 Performance Metrics & Scoring

  • What: Evaluate agent effectiveness using quantitative metrics.
  • How: Assign performance scores (accuracy, speed, confidence) and track trends.
  • Why: Identify underperforming agents and adjust automation levels accordingly.

2.4 Root Cause Analysis & Self-Healing

  • What: Identify why failures happen and trigger automated corrections.
  • How: Use error logging, anomaly detection, and adaptive learning.
  • Why: Minimize human intervention and improve self-recovery.

Example:

  • If an agent’s classification accuracy drops below 80%, automatically retrain the model on the latest feedback.

3️⃣ Implementation Plan in Power Automate

Step 1: Enable Structured Logging

  • Capture agent actions in database, Azure Blobs, Dataverse, or SharePoint.
  • Store:
    • Agent name, action, input, output, timestamps.
    • AI confidence scores, human corrections, workflow status.

Step 2: Real-Time Monitoring & Alerts

  • Use Power Automate’s monitoring tools or Azure Application Insights.
  • Set up alerts for:
    • High error rates.
    • Slow response times.
    • Frequent human overrides of agent outputs.

Step 3: Create Agent Performance Dashboards

  • Power BI integration to visualize:
    • Agent accuracy trends.
    • Workflow bottlenecks.
    • Automation confidence levels.

Step 4: Implement Self-Healing Mechanisms

  • Trigger auto-retraining when performance drops.
  • Adjust automation levels dynamically based on agent reliability.

4️⃣ Long-Term Optimization

1. Continuous Improvement Loop

  1. Log agent behavior and collect feedback.
  2. Analyze data trends for optimization.
  3. Retrain AI models based on agent scoring.
  4. Adjust automation thresholds dynamically.

2. Scaling Observability

  • Extend to multi-agent systems (e.g., coordinating across multiple workflows).
  • Introduce AI-driven workflow tuning (e.g., intelligent decision-routing based on agent performance).

5️⃣ Next Steps

  • Where should we store agent logs? (database, Azure Blobs, Dataverse, SharePoint?)
  • What thresholds should trigger alerts? (High error rates, long processing times?)
  • Do you want automated model retraining or manual review checkpoints?

With agent observability at the core, AgenticOps becomes a self-optimizing, transparent, and explainable automation system! How’s your agent observability? Want to discuss mine in more details, give me a poke. 🚀

Creating an AgenticOps Powered Email Workflow

Workflow Overview

This is workflow seems simple enough to wrap our heads around. It is complex enough to get a feel for how to build an AgenticOps workflow. You do not need to use an overly complicated platform. Yet, I’m very technical and analytical in my old age. This is easy for me, but it may be harder if you don’t deal with building with technology daily. However, anyone with a little patience and problem-solving ability can handle it.

Here’s the workflow:

Trigger: An email is received (via Outlook connector).

Agent 1: Summarization Agent

  • Extracts key information from the email (e.g., sender intent, action items, important context).
  • Uses Azure OpenAI (GPT/Copilot) or AI Builder for summarization.

Agent 2: Sentiment Analysis Agent

  • Analyzes sentiment (e.g., Positive, Neutral, Negative, Urgent) using:
    • Power Automate AI Builder
    • Azure Cognitive Services Text Analytics
    • GPT-based prompt for sentiment classification
  • Adds a Sentiment Label to guide prioritization.

Agent 3: Categorization Agent

  • Classifies emails into categories such as:
    • Support
    • Sales
    • Urgent
    • Inquiry
    • Spam
  • Uses AI-based classification.

Agent 4: Priority Routing Agent

  • Uses Sentiment + Category to assign a priority level:
    • High Priority (Urgent & Negative Sentiment) → Immediate Action
    • Medium Priority (Neutral Sentiment) → Regular Workflow
    • Low Priority (Positive Sentiment) → Can be delayed

Agent 5: Reply Generation Agent

  • Generates an AI-powered response:
    • Uses Azure OpenAI GPT/Copilot
    • Includes pre-defined templates
    • Formats placeholders (e.g., Client Name, Ticket ID)

Agent 6: Review & Edit Agent

  • Reviews AI-generated response (human or AI).
  • Provides edit suggestions and tracks changes.

Agent 7: Approval Agent

  • Final approval for sending response.
  • Decision options: Approve, Edit, Reject.

Decision Point: Manager (AI or Human)

  • If approved → Send Email
  • If edited → Return for Review
  • If rejected → Escalate for Manual Handling

Action: Send, Revise, or Flag for Manual Review

Implementation in Power Automate

Step 1: Create Power Automate Flow

  • Trigger: New email arrives in Outlook.
  • Filter: Exclude spam using AI-based rules.
  • Extract: Email Body, Sender, Subject for processing.

Step 2: Summarization Agent

  • Use Azure OpenAI GPT, Copilot, or AI Builder for summarization.
  • Return key points from email.

Step 3: Sentiment Analysis Agent

  • Call Azure Cognitive Services – Text Analytics API
  • Classify sentiment: Positive, Neutral, Negative, Urgent
  • Store Sentiment Score & Label

Step 4: Categorization Agent

  • AI-based classification into Support, Sales, Urgent, Inquiry, Spam

Step 5: Priority Routing Agent

  • If Urgent & Negative Sentiment → Escalate Immediately
  • If Positive Sentiment → Queue for Later
  • If Neutral Sentiment → Proceed Normally

Step 6: Reply Generation Agent

  • Generate reply with GPT, Copilot, or AI templates
  • Auto-insert placeholders like [Client Name], [Ticket ID]

Step 7: Review & Edit Agent

  • AI or human suggests modifications to response.
  • Changes are stored in Dataverse or SharePoint.

Step 8: Approval Agent

  • Approve, Edit, or Reject email response.

Step 9: Decision Point (AI Manager or Human)

  • If Approved → Send Email Automatically.
  • If Rejected → Manual Review or Escalation.

Enhancements & Extensions

✅ Logging & Monitoring

  • Track workflow execution, decisions, and feedback.
  • Store logs in Dataverse, SharePoint, or SQL.

✅ Adaptive Workflow

  • Urgent Emails: Send Teams Notification for immediate action.
  • Low-Priority Emails: Add to review queue for later processing.

✅ Integration with Teams

  • Notify Teams channel if approval is required.
  • Allow human managers to approve via Teams.

🚀 Final Questions Before Implementation

  1. Deployment Choice
    • Power Automate Cloud (Fully automated & integrated with Outlook)?
    • Power Automate Desktop (For more local processing)?
  2. Review Process
    • Do you want a human-in-the-loop for reviewing AI responses?
    • Or should this be fully autonomous?
  3. AI Model Preference
    • Azure OpenAI GPT-4/Copilot for Summarization, Categorization & Reply?
    • Azure Cognitive Services for Sentiment Analysis?

Should I write the detailed steps? Need help building this workflow or something like it, let me know, and we can talk it out! 🚀

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.