#ai-agents
Every post tagged "ai-agents".
303 posts · showing 51–60
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Your Two Reviewer Agents Disagree. Who Breaks the Tie?
Running the same document past two agents is easy. Deciding what happens when they return opposite verdicts is the part most multi-agent designs quietly leave undefined — and the default resolution is usually the worst one available.
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Cached or Re-Called? Giving Every Agent Tool a Staleness Budget
An agent that reads a value once and acts on it four minutes later isn't reasoning badly — nobody told it how long that value stays true. Per-tool staleness budgets, and the one rule that matters most before a write.
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One Worker Pool or Two? Separating Fast Agent Runs From Slow Ones
A handful of long research runs can starve hundreds of eight-second ticket replies without a single error firing. The fix isn't more workers — it's deciding, at admission, which lane a run belongs in.
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Your Agent Retried the Tool Call. The Customer Got Charged Twice.
A timed-out tool call is not a failed one — it is an unknown one. Here is why "just retry" quietly doubles real-world side effects, and where to put the idempotency check so it cannot.
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Blackboard or Mailbox: How Should Three Agents Share What They Learn?
Two agents discover the same fact; a third contradicts both and nobody notices. The shape of your shared state — a common board or point-to-point messages — decides which of those happens.
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Your Agent Has One manage_calendar Tool. That's Why It Deleted the Wrong Meeting.
A single do-everything tool looks tidy in the schema and hides four separate problems: guessed actions, unvalidated payloads, permissions that can't tell reading from deleting, and traces that flatten three risk levels into one name. Here's where to cut instead — and where splitting makes things worse.
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Your Agents Asked 40 Questions This Week. Which Ones Should Have Reached a Human?
Human attention is the one resource in an agent deployment that never shows up on the bill — so agents spend it freely. Here's a three-way routing rule, a threshold you can actually tune, and the two metrics that tell you when your agent is asking too much or too little.
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Map-Reduce or Sequential Refine? How an Agent Should Read 400 Pages
Chunking 400 pages of contracts forces a choice most teams make by accident. One shape quietly drops evidence as it goes; the other can never spot a conflict between two documents — and the fix is not the pattern, it's what the map step returns.
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Draining a Data Backlog: Should the Agent Batch Rows or Take Them One at a Time?
The ratio of preamble tokens to row tokens decides whether chunking a backlog saves an order of magnitude or nothing at all — and it's the alignment, retry amplification and blast radius that decide whether you should anyway.
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Who Decides the Agent Is Done? Three Stop Conditions, and Why "The Model Said So" Isn't One
Most first agents end their loop when the model stops calling tools — which means the model, not you, decides what "done" means. Here are the three stop conditions a real loop needs, and the twenty lines that turn a silent wrong answer into another iteration.