#ai-agents
Every post tagged "ai-agents".
304 posts · showing 201–210
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Computer Use: When the Agent Drives a Whole Desktop
Clicking is easy; knowing what to click is a perception problem solved from a screenshot. The failures are modals, focus loss, and scroll state — and an agent that assumes its click landed compounds errors silently.
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Designing the First Turn: What Happens Before Any Tool Call
Everything after it inherits the frame it sets. Four things belong in turn one — and when a run fails at turn twelve, this is usually where the defect was introduced.
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Why Turn 20 Costs More Than Turn 2
A six-turn run whose final context is 12,000 tokens sends 42,000. That gap is agent economics — and it makes an early oversized tool result the most expensive thing in your run.
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Running Agents on a Schedule: What Changes Without a User
An agent someone triggers has a safety property nobody designs for: a person is watching. Put it on a schedule and that goes away — along with the feedback loop that was catching the errors.
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Tracing Standards for Agents: What Fits and What's Missing
A run is a trace and a turn is a span — the mapping is cleaner than expected. Two things break: payloads far too big for span attributes, and sampling that discards exactly the runs you need.
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The QA Agent That Wrote Tests That Always Passed
Coverage climbed, the build stayed green, and a real regression sailed through. An agent asked for passing tests writes passing tests — and the easiest passing test verifies nothing.
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Content Boundaries: What Your Agent Should Refuse, and How to Test It
Providers handle the general case. Scope, regulatory limits, commitments, and how your agent represents you are yours — and the test suite needs both halves, or you'll ship something that refuses everything.
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Eval Cases Where the Right Answer Is "I Don't Know"
Suites measure whether the agent got it right, never whether it should have answered at all. Five case types where the correct response is a question — plus the paired cases that stop you from just making it timid.
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Who Owns the Agent? The Team Structure Question
Prompts need domain expertise; tools need engineering; evals are specification. That split doesn't match team boundaries, which is why agent ownership goes ambiguous — and ambiguous ownership shows up as quality nobody watches.
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How Many Agents Is Too Many?
Every addition is locally justified, and then the system costs more and answers worse than a simpler one would. Four measurable signals you've overshot — and the merge experiment that settles it.