#ai-agents
Every post tagged "ai-agents".
303 posts · showing 61–70
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Read, Draft, Send: Where to Draw the Permission Line in a Personal Email Agent
Most email agents get permissions wrong by granting them per tool. The safer line runs through the arguments — who the recipient is, how many there are, and whether the thread was already open.
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What Should Your Agent Forget? Give Every Memory an Expiry Rule
Agent memory systems have a write verb and a recall verb, but no verb for "stop believing this." Here's how to decide at write time what makes each memory stop being true — and why expiry should downgrade a fact rather than delete it.
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The Worker Agent Reported Success. Where Should the Check Live?
A worker agent's "done" is a sentence, not evidence — and in effectful work the gap between the two is where silent failures live. Three places to put the verification, and the one most teams pick that buys nothing.
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How Should a Team Review a Tool Description Diff? Treat It Like a Schema Migration
A six-word edit to a tool description is a behavior change with no compiler to catch it. Borrowing the review checklist teams already use for schema migrations turns an unreviewable prose diff into something you can actually approve.
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Retry, Reroute, or Hand Back: Budgeting an Agent's Second Chances
A tool call fails and the loop tries again — five times, forty seconds, and still nothing usable for the user. Retrying is only one of three legal responses, and picking the wrong one turns a fast failure into an expensive one.
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The Tool Returned 200 and Nothing Happened
A calendar agent reported success on a meeting that landed at 3am, on the wrong calendar, with no invitation sent. The API did exactly what it was asked — and the return value gave the agent no way to know.
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Let the Model Pick the Transition, Not the Tool
A returns agent that can issue a refund before it has checked eligibility will eventually do exactly that, and no amount of prompt wording reliably stops it. Making the call unavailable is a design decision, not a prompt one.
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Extraction Was Never the Hard Part: An Invoice Agent, Rebuilt
The first version had the model read invoices and type the numbers. The rebuild started after the numbers were already read — which is where the actual work in accounts payable has always been.
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Where Should the Agent Loop Live? Framework, Job Queue, or Workflow Engine
The choice isn't about which framework has the nicer API. It's about what a crash at step six costs — and durable execution engines carry a constraint that fights the way agents are written.
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Your 95% Step Accuracy Is a 60% Agent
Compounding explains part of the gap between a green eval dashboard and a broken agent. The rest is that per-step scoring counts a step as correct when it succeeds on a premise that was already wrong.