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AI Agents Playbook for Tech Leads

1Fundamentals

Agent Fundamentals

2Building Blocks

3Design Patterns

4Operations

5Optimization

Playbook Overview

This playbook distills my end-to-end framework for designing, building, and operating production-grade AI agents. It blends architectural blueprints, operational checklists, and real-world battle scars into a structured guide for technical leaders.

Who This Is For

CTOs and Tech Leads preparing to ship their first agent-powered product
AI Engineers migrating from RAG/Pipelines to agentic systems
Engineering teams that need an opinionated framework for experimentation, evaluation, and governance

What You Will Learn

By the end of this playbook you will have:

A precise definition of what a true AI agent is (and is not), plus a reusable blueprint for reasoning, planning, and tool integration.
A production-ready AgentOps workflow covering instrumentation, observability, evaluation harnesses, and governance guardrails.
Frameworks and scorecards that help qualify agent use cases before you commit to building.

A Note on This Playbook

This playbook is a sincere attempt to provide a practitioner's blueprint for production Agentic AI, moving beyond the code to explore the critical decision-making, trade-offs, and challenges involved.

Important Disclaimers:

On Authenticity: The methodologies and frameworks shared here are drawn directly from my professional experience.
On Collaboration: These posts were created with the assistance of AI for diagram, code and prose generation. The strategic framing, project context, and real-world insights that guide the content are entirely my own.

Chapters

Agent Fundamentals

Clarifies what separates true AI agents from augmented LLMs and outlines the architecture that enables autonomous decision-making.

LLM - Prompts, Goals, and Persona

Learn how to design the agent brain through effective prompting, goal definition, and persona crafting for optimal LLM performance.

Agent Memory

Understand how to implement short-term and long-term memory systems that enable agents to learn, recall, and improve over time.

Tool Use and Integration

Master the patterns for integrating external tools and APIs that extend agent capabilities beyond the base LLM.

Data Management and RAG

Explore strategies for connecting agents to enterprise data through RAG, managing knowledge bases, and ensuring data quality.

Orchestration and Task Decomposition

Learn how agents sequence and coordinate actions by breaking complex goals into manageable subtasks.

Agentic Patterns

Discover composable design patterns for building AI agents, from structured workflows to dynamic autonomous systems.

Context Engineering

Master the art of filling the context window with optimal information to maximize agent effectiveness and minimize failures.

Evaluations

Build comprehensive evaluation frameworks to measure agent performance, quality, and reliability in production.

Guardrails

Implement safety constraints and policy enforcement to ensure agents operate within acceptable boundaries.

Monitoring and Observability

Set up instrumentation and observability systems to track agent behavior, performance, and decision-making in production.

Human-in-the-Loop

Design effective human oversight mechanisms for critical decisions and continuous agent improvement.

Deployment and Scaling

Deploy agents to production and implement scaling strategies to handle increasing load and complexity.

Trust and Ethics

Build trustworthy AI agents that align with ethical principles and maintain user trust through transparency.

Security

Secure agents against prompt injection, data leakage, and abuse while protecting integration points.

Cost Optimization

Implement strategies to reduce operational costs while maintaining agent performance and quality.

Latency Optimization

Optimize response times and throughput for real-time agent interactions and high-volume scenarios.

Production Best Practices

Navigate common production challenges and apply proven best practices from real-world agent deployments.

Work With Me

I bring hands-on experience delivering production MLOps and GenAI systems at moderate scale—with minimal infrastructure footprint and cost-effective architectures. I'm excited to collaborate on building next-generation Agentic AI systems. Whether you need expertise in MLOps, GenAI, or Agentic AI—let's connect.