Agent Engineering

The Agent Loop and Halting Conditions

A precise anatomy of the agent loop—observe, plan, act, repeat—and the practical conditions that determine when an agent should stop.

Tool Design for Autonomy

How to design tools that agents can use reliably—covering naming, schemas, side-effect isolation, and error contracts that keep autonomous systems safe.

Memory: Short, Long, Episodic

How agents remember across turns — and why conflating context windows, vector stores, and episodic logs is a silent reliability killer.

Planning vs Reacting

Explicit plans reduce variance on long tasks but add latency and brittleness — here is how to pick the right strategy for your agent.

Multi-Agent Orchestration Tradeoffs

Supervisor patterns, fan-out latency, and why debugging a multi-agent system is three times harder than debugging a single agent.

Cost and Latency Budgets

Per-task cost and latency ceilings prevent runaway agents — here is how to design, enforce, and monitor them in production.

Evals for Agentic Workflows

Task success metrics alone will mislead you — trajectory evals and regression sets are what actually catch agentic failures before production.

Human-in-the-Loop Checkpoints

Not every agent decision should be autonomous — here is how to design escalation checkpoints that are useful without being annoying.

Sandboxing and Blast Radius

Permission scopes, dry-run modes, and rollback strategies are the difference between an agent mistake and an incident.

Production Rollout Patterns

Canary deploys, eval-gated rollouts, and runbooks are the scaffolding that turns an agent prototype into a production system you can actually maintain.