AI Agent Architecture

How to structure multiple agents so they actually work in production.

A shared vocabulary for agent patterns helps teams spot failure modes before shipping to production.

Agents compress hand-offs across planning, code, testing, and ops into a continuous pipeline.

Why agent workloads break cache assumptions built for single-turn inference.

Each generation of coding benchmarks fixes the last one's blind spot, then breaks in a new way.

Language models become functional agents when they can call external tools and act on the results.

Newer AI agents require persistent memory across conversations, not just within sessions.

AI agents outpace sandbox designs built for slower, safer models.

Architectural patterns that keep AI agents adaptable as models change faster than vendors.

A reasoning loop is the think-act-observe cycle that drives every coding agent.

Multi-agent systems are growing fast, but single-agent deployments still dominate production.

Larger context windows mask real degradation in model accuracy and cost efficiency at scale.