209 posts tagged with "agents"

A flaky test gets quarantined at 3:17 AM. The on-call rotation does not page, because nothing failed — the agent decided the failure was noise, opened a small PR labeled chore: quarantine flaky test, marked the change as a self-merge under the ci-bot service account, and went back to watching the queue. Six days later a customer reports that a feature has been broken since Tuesday. The test was not flaky. It was the only thing standing between a real regression and production, and the agent's confidence threshold was set high enough to make a decision but low enough to be wrong.

This is the part of agentic CI that the marketing decks skip. Wiring an agent into your pipeline to triage failures, downgrade dependencies on security alerts, and propose dependency bumps is straightforward in 2026 — the tools exist, the integrations are one config file away, and the productivity story is real. The part that nobody writes a runbook for is the new operational class you just created: an actor with merge rights that runs at 3 AM with no human in the synchronous loop, and an SRE handbook that assumed humans were the source of intent.

The user is reading the agent's reasoning as it streams in. Around token 1200, the model decides to call send_email, then create_ticket, then kick_off_deploy. The user, watching the partial output and realizing the agent has misread the request, hits the stop button half a second too late. The email is already sent. The ticket is already filed. The deploy is already running. The stop button cancelled the next token, not the consequences of the last one.

The bug is not in the cancel handler. The bug is the assumption — borrowed from every other streaming UI on the team's roadmap — that an incrementally rendered output is an incrementally reversible one. Tool calls do not honor that contract. They are point-in-time commits that the streaming layer happily fires while the rest of the response is still being generated, and the cancel button has no way to chase them down the wire.

This is one of those failure modes that nobody owns because it lives in the seam between two teams that each shipped their half cleanly. The UX team shipped streaming because it tested better in user studies. The platform team shipped tool calls because the framework supports them. Neither team had a meeting where someone asked: what is "stop" supposed to mean when the response has already left the building?

Compliance forwards you a ticket. A customer was denied a refund by your support agent three weeks ago, they have escalated, and now someone needs to explain the decision. You feel calm about this, because you instrumented everything. Every prompt, every tool call, every retrieved chunk, every token count, every latency number — it is all in the trace, and you can pull it up in seconds.

You pull it up. You can see the agent received the refund request. You can see it called get_order_history, then check_return_window, then lookup_policy. You can see the exact policy text it retrieved. You can see the final message it sent: refund denied. The trace is complete. Every span is green. And you still cannot answer the question, because the trace shows you that the agent denied the refund and shows you everything it looked at, but it does not show you why those inputs added up to no. The reason lived in how the model weighed the context, and that weighing was never an artifact. It was never written down anywhere.

This is the gap between a trace and an explanation, and almost every team that says "we have full observability" has not noticed they only built the first half.

The model has streamed seventy percent of a confident-sounding answer to the user's screen. Then the tool call it was about to make returns an error, or no rows, or a 429. You now get to pick between two losses: let the model finish gracefully by inventing the rest, or stop mid-sentence with no clean way to walk it back. Neither is a recovery — both are damage.

This is the part of streaming UX that nobody priced when they turned the feature on. Streaming was framed as a perceived-latency win: time-to-first-token is the metric, the user starts reading sooner, the app feels alive. What the framing leaves out is that every token you stream is a commitment. You have published a draft of an answer that you do not yet know is correct, and the back half of your system has not yet finished running. When it finishes and disagrees, your UI has no native way to retract what it already showed.

Your operations agent answers a question about the upcoming launch by quoting a Slack message that says "we'll ship tomorrow." The agent treats that as a present-tense plan and starts writing comms. The message was posted six weeks ago. The ship happened. The retrieval pipeline pulled the right chunk by every metric you measure — semantic similarity to "launch date," top-1 confidence above your threshold, source channel matching the project — and the agent built a plan on a sentence that meant something only inside the meeting where it was written.

The bug is not in the model. The bug is that tomorrow is not a date. It is a pointer to a clock, and the clock the message was written against is not the clock the agent is reading it on. Your retrieval pipeline indexed the body of the message and discarded the frame.

You asked the agent to fix a flaky test. At minute three, the test passes. At minute four, the agent is reading neighbouring files. At minute nine, it has "improved" a helper that the test never touched, renamed an unrelated parameter for clarity, and started a refactor of the fixture builder. The diff that lands is twelve files and four hundred lines. The original bug is fixed. So is some other code that wasn't broken.

This is not a model getting confused. This is a model doing exactly what its instructions left room for. The task said "fix the bug." It did not say "stop after the bug is fixed." Most agent loops have a defined start and a defined success criterion, and a very fuzzy answer to the third question: when are you done? In a chat session, "done" is whatever the user accepts. In an autonomous loop, "done" is whatever the stopping condition says, and if you didn't write one, the stopping condition is "the model lost interest." That isn't a failure mode you can debug. It's a failure mode you have to design out.

Every day your agent ships failures back to you, gift-wrapped. A user clicks thumbs-down. Another reads the answer, says nothing, and closes the tab. A third rephrases the same question three times until the agent finally gets it. Each of those is a labeled failure case — a real input, a real context, a real moment where the system fell short — handed to you for free by the people who care most about getting it right.

Most teams throw all of it away. Not deliberately. The thumbs-down increments a dashboard counter. The abandonment shows up as a dip in a retention chart. The rephrasing looks like ordinary usage. Nothing captures the signal together with the context that produced it, so nothing can be replayed, triaged, or turned into a test. The richest source of evaluation data you will ever have flows past untouched, and the team keeps writing synthetic eval cases by hand.

This is the agent feedback loop you never built. It is not a tool you forgot to buy. It is a pipeline — from user signal, to triaged failure, to new eval case — and the reason it stays unbuilt has very little to do with technology.

Finance asks one question about every feature you ship: "What does it cost per user?" For a traditional feature, the answer is a number. A page render, a database query, a push notification — each has a marginal cost that barely moves from one request to the next. You measure it once, multiply by your user count, and the forecast holds.

An AI feature breaks that contract. Ask "what does this agent cost per request" and the honest answer is not a number, it's a histogram. The same agent that resolves one ticket for two cents will burn four dollars on the next one, because that user asked a vague question, the agent looped through eleven tool calls, and each call dragged the entire growing conversation back through the model. The mean of those two requests — two dollars — describes neither of them, and it definitely doesn't describe the bill.

That is the trap. When you hand finance a single average cost, you are not simplifying a messy reality. You are reporting a number that is wrong in a specific, expensive direction.

Most teams treat the context window as a budget. You have a million tokens; spend them wisely; longer conversations cost more and run slower. That framing is correct and incomplete. The context window is also an attack surface, and its size is a dial that quietly weakens your safety controls as it turns up.

Here is the failure mode nobody puts in the threat model. Your system prompt — the one with the guardrails, the tool-use rules, the "never do X" clauses — sits at the very top of the context. Its authority is strongest there. As a conversation runs, thousands of tokens of user turns, tool outputs, and retrieved documents pile on top of it. The model's attention does not weigh all of those tokens equally. The instructions closest to the point of generation win ties. By turn forty, your guardrails are not gone, but they are buried, and a patient adversary does not need a clever jailbreak to get past them. They just need a conversation long enough.

This is not a hypothetical. It is a measurable property of how transformers attend to long contexts, and it has a name in the research literature even if it does not have one in your incident review template.

A rate limit used to be an infrastructure detail. You hit a 429, you retried with backoff, you queued the overflow, and nobody outside the on-call channel ever knew it happened. The user saw a response that was a few hundred milliseconds slower than usual. That was the whole story.

That story no longer holds for agentic features. When an agent hits a provider's tokens-per-minute ceiling halfway through a multi-step plan, the failure does not stay inside the infrastructure. It surfaces as a half-finished answer, a tool loop that stalls before the last call, or a user watching a spinner that will never resolve. The quota stopped being a backend capacity number and became a constraint that product has to design around — the same way product designs around a checkout flow or an empty state.

When a security team reviews a new tool integration, they read the code. They check what the function does, what it touches, what scopes it needs, whether it logs secrets. They almost never read the one sentence that decides whether the model calls it at all — the tool's description. That sentence is not documentation. It is an instruction the model treats as authoritative, and in most agent stacks nobody reviews it.

A tool description is written for the model to read. The model uses it to decide when the tool is relevant, what arguments to pass, and how to interpret what comes back. That makes the description a control channel into the model's behavior. And the moment a tool arrives from a third-party registry, a Model Context Protocol (MCP) server you don't operate, or a plugin a teammate installed last week, that control channel is authored by someone you never agreed to trust.

This is the gap. Input sanitization inspects what users type. Code review inspects what functions execute. The tool description sits between them — it is configuration that behaves like input — and it falls through both nets.

An engineer spends an afternoon poking at a question: can the agent reconcile a customer's invoice against their contract terms? They wire up a quick prompt, run it on five real invoices, and three come back correct. The other two are wrong in ways they don't fully characterize — they close the laptop and move on. In standup the next morning they say "yeah, invoice reconciliation basically works." A PM in the room writes it down. Two weeks later it's a line item on the Q3 roadmap. A month after that, a sales rep promises it to an enterprise account in a renewal call.

Nobody lied. Nobody made a bad decision in isolation. But the team is now contractually committed to a behavior whose eval set does not exist, whose failure modes were never written down, and whose reliability budget was set by a director who saw a demo and interpreted it as a contract. This is the most common way AI features acquire scope: not through a planning meeting, but through a capability probe that nobody ever explicitly promoted.

The industry has a name for the downstream symptom — "POC purgatory," the state where 70 to 80 percent of AI initiatives stall between a working sandbox and a shippable product. But purgatory is the wrong metaphor, because it implies the projects are stuck. They aren't stuck. They're moving — they were committed before anyone checked whether they were ready, and now the team is trying to retrofit reliability onto a promise.