Architecture patterns, industry analysis, and engineering practice for teams building production AI agents.
This site covers the architecture, engineering practice, and industry direction of agentic AI systems: systems where LLM agents plan, use tools, and act with real autonomy. The focus is technical and centers on the Google AI stack: Vertex AI, Gemini, the Agent Development Kit, and the Agent2Agent protocol. Expect orchestration patterns, failure modes, evaluation methods, and the tradeoffs that do not show up in a demo.
I also advise engineering and product teams building these systems in production, helping them close the gap between a working prototype and something they can trust.
Core topics
Agent orchestration
Single-agent loops, planner-executor patterns, and multi-agent systems: when to use each one.
Tool use and reliability
Tool schemas, error handling, and the gap between a demo that works and a system that works at scale.
Memory and context
Context management, retrieval, and state across long-running or multi-turn agent sessions.
Evaluation and observability
How to measure whether an agent is actually working, and how to see what it did when it is not.
Guardrails and bounded autonomy
Step limits, budget limits, and human checkpoints: how to bound autonomy without killing it.
Enterprise adoption and platform strategy
How organizations move from a single agent proof of concept to a platform other teams build on.
How an agent loop actually works
In production, this loop runs inside explicit boundaries: step limits, cost budgets, and human checkpoints at the places where a wrong action is expensive to undo.
Latest
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Agent Orchestration Patterns: A Field Guide
A practical breakdown of the common ways to structure multi-agent and single-agent LLM systems, and when to reach for each one.
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Why Tool Use Breaks in Production (And How to Fix It)
The gap between a tool call working in a demo and working reliably at scale.
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The State of Agentic AI: Adoption vs. Hype
A framework for separating what is actually shipping in production from what is only being claimed.
Where to start
- Architecture: deep dives on agent design patterns, orchestration, memory, tool use, evaluation, and reliability.
- Industry: commentary and analysis on where agentic AI is heading, and what’s signal vs. noise.
- Consulting: how I work with teams, and how to get in touch.
Why read this
Most agentic AI content optimizes for the demo: an agent that looks impressive in a five-minute clip. This site optimizes for the opposite problem, the parts that only show up after a system is running in front of real users with real consequences for failure. Every pattern here is grounded in implementation detail and production tradeoffs, not speculation about what agents might eventually do.
Consulting
I help engineering and product teams take agentic AI systems from prototype to something they can trust in production, with particular depth in the Google AI stack. See how I work with teams for engagement details.
Connect
Find me on GitHub or LinkedIn, subscribe to the RSS feed, or reach out at ismailkhan.dev@gmail.com.