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TAGGEDAGENTS 11 POSTS

Posts about AGENTS.

2026.06.23 AGENTS

Tool-calling reliability plateaus at your tool descriptions, not your model.

An agent that calls the wrong tool too often looks like a model problem, so teams reach for a bigger model or a fine-tune. The wrong calls cluster where two tools look near-identical to the model — and rewriting or de-duplicating the ambiguous descriptions recovers double-digit accuracy a bigger model does not. The tool interface is a primary ceiling on tool-calling reliability, and the model's own choice margin is a usable pre-execution alarm.

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2026.06.03 AGENTS

Shipping an agent: canaries and rollback for prompts.

A one-word change to a system prompt can move accuracy by dozens of points, and a provider's model update can regress your app overnight. A prompt or model swap is a deploy. Give it a staged rollout and a one-action rollback path.

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2026.06.01 AGENTS

Cost observability for an agent fleet.

The monthly inference bill arrives as one number, and nobody can say which agent, which customer, or which tool spent it. Agent cost is too variable to estimate and has to be attributed after the fact — per run, per tool, per tenant. The layer most stacks skip.

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2026.05.29 AGENTS

Human-in-the-loop checkpoints without killing throughput.

An agent that asks permission for everything trains its reviewers to rubber-stamp, and the one dangerous action slips through in the noise. Approval gates belong on consequence and on uncertainty — not on every step. Where to put them.

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2026.05.18 AGENTS

Tool design for agents: the schema is the prompt.

A tool's name, description, and JSON schema are the only thing the model sees — it picks and fills tools from those words alone. Treating that surface as API plumbing instead of as prompt is why agents call the wrong tool. How to design it.

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2026.05.16 AGENTS

Agent memory is a microservice, not a vector store.

"Add memory" usually means "dump everything into a vector DB." Real agent memory is tiered, has a write/forget/consolidate policy, and runs as a service the agent calls — not a library bolted into the loop.

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2026.05.16 AGENTS

Your APM cannot see your agent failing.

Request traces and dashboards were built for request/response services. The ways an agent fails — a tool returning 200 with garbage, a truncated context, a looping planner — trip none of them. What agent-native observability has to capture.

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2026.05.13 AGENTS

Deterministic replay: debugging agents that will not reproduce.

An agent run is non-deterministic — sampling, tool responses, and timing all vary — so a bug seen once may never recur. Deterministic replay records every non-deterministic input so the run can be replayed exactly.

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2026.05.07 AGENTS

Context engineering beats prompt engineering for long-running agents.

For a long-running agent, the system prompt is a small part of the problem. The real discipline is managing the context window across the whole run as a budget — keep, drop, compact, retrieve.

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2026.04.14 AGENTS

Multi-agent systems are usually one agent too many.

Splitting a task across coordinating agents adds context-handoff loss, compounding latency and cost, and a wider failure surface — overhead that usually exceeds the benefit. Start with one agent.

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2026.03.08 AGENTS

Notes on agent budgets: why "let it think longer" is a bug.

An agent that hits a wall and asks for more compute is not reasoning. It is panicking. The budget is part of the spec, not a fallback.

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