Supervisor-worker agents succeed or fail on decomposition. Learn when to create workers, how to structure task packets, why worker context design matters, and how to trace orchestration in production.
Most multi-agent failures happen at transfer boundaries. Learn how to design a compact handoff contract so the next agent gets the right intent, constraints, status, evidence, next action, and stop condition without inheriting noisy transcript history.
Most so-called multi-agent systems are really workflows. A true multi-agent system starts when agents can delegate or hand off work at runtime based on live task state. Here is the practical mental model developers need.
Multi-agent systems are not a default upgrade. Learn when multiple agents actually help, why one well-instrumented agent is often enough, and how to choose based on observable bottlenecks instead of architecture fashion.
Memory safety is not just a privacy concern. In agent systems, bad memory changes future behavior. Learn how typed schemas, policy-gated writes, auditable reads, and safe deletion make memory part of runtime correctness.
Personalization in agents works best when memory is narrow, typed, reviewable, and clearly separated from sensitive systems. This article explains how to store useful preferences without creating invasive or unsafe agent behavior.
Memory bugs in AI agents usually come from bad writes, not weak recall. Learn how to design write policies that decide what an agent should persist, when it should write, and when it should not.
Long context windows do not solve long-running agent state. This article explains why summarization is really state compression, how to design structured summaries, when to trigger compaction, and how to detect behavioral drift before bad memory causes wrong tool calls or broken constraint handling.
Retrieval is not memory. In production AI agents for developers, retrieval should stay a read path to external systems, while memory remains a write path for learned continuity like preferences, instructions, and successful procedures.
AI agents usually do not fail with a dramatic crash. They fail quietly through wrong tool calls, invalid arguments, retry storms, looping behavior, and weak recovery. This article explains where the agent loop breaks and how to design traces, guardrails, limits, checkpointing, idempotency, and evals that catch incidents before users do.