AI Agent Orchestration
Architecture for multi-step AI agents covering planning, tool use, memory, evaluation, and human-in-the-loop controls.
Components
Considerations
Alternatives
Complexity
Fit
When this blueprint fits
And when to walk away from it
When to use this
Your AI feature is more than a single model call: it plans steps, calls tools, accumulates context, and makes decisions over time. Coding assistants, research agents, and workflow automation all live here.
When NOT to use this
If a single LLM call with retrieval solves the problem, do not introduce an agent loop. Agents add cost, latency, and failure modes proportional to step count.
Architecture
System components
Key building blocks of this architecture, layered from infrastructure up.
Planner
Tool Registry
Memory
Execution Runtime
Human-in-the-Loop
Eval and Replay
Cost and Step Bounds
Planning
Critical considerations
The things I have learned the hard way and would not skip on the next build.
Options
Alternative approaches
Where I would consider a different shape entirely, with the trade-offs spelled out.
Implementation
Related playbooks
Step-by-step guides for the harder parts of this architecture.
Shipping AI Features Without the Hype Tax
Most AI features ship as a demo that survives one round of investor questions and then quietly dies in production. This is the discipline that gets AI features past that wall: small scope, real evals, careful rollouts, and instrumentation that catches drift early. The same loop I run when I add AI capabilities to an existing product, on a real timeline with real users.
Building RAG Applications
Retrieval-augmented generation looks simple in a demo and stays simple until your knowledge base is bigger than a thousand documents, chunks overlap badly, or relevance scores stop making sense. This is my end-to-end RAG playbook: document processing, embedding pipelines, retrieval tuning, prompt design, and the evaluation harness that tells you whether changes are actually improving results.
In practice
Related case studies
Where I have applied this blueprint to real builds and what changed in practice.
Thinking
Related insights
Essays where I argue the trade-offs behind the choices in this blueprint.
Prompt Engineering for Production
Production prompts need to be reliable, testable, and maintainable. Here's how to treat prompts as code with proper engineering practices.
An LLM Evaluation Framework That Works
How to systematically evaluate LLM applications with a practical framework covering automated metrics, human evaluation, and continuous monitoring.
Need help implementing this blueprint?
I help teams adapt blueprints like this to their specific requirements and ship from planning through production.
AI Systems
More in this category
Other blueprints with overlapping concerns.
AI Application Architecture
Architecture for production AI applications with model serving, RAG pipelines, evaluation, and cost controls that survive contact with real users.
RAG Application Blueprint
Reference architecture for retrieval-augmented generation apps covering embedding pipelines, vector search, prompt orchestration, and evaluation.