Articles, tutorials, and guides tagged Reliability.
Reasoning traces are the feature everyone turned on this month and nobody planned for. Handled wrong, they quietly triple your token bill, dump intermediate reasoning full of customer data into your logs, and break your tool loop in a way that looks like the model got dumber. Here is how to treat extended thinking as something you manage at the boundary, not something you print.
A ReAct-style agent calls the same search tool, gets the same unhelpful result, decides another identical call will help, and does it again. Seventy times. It never crashes and never finishes, it just burns tokens going nowhere until a timeout or your bill catches it. A hard step ceiling is a backstop, not a fix. What you want is a guard that notices the agent has stopped making progress and breaks the cycle in seconds.
Your eval suite is green. Every case passes. Then a real user has a six-turn conversation with your agent and it forgets what they said in turn one, asks for information they already gave, and quietly breaks a policy under pressure. Single-shot evals test a single prompt. Production is a conversation. Drive your agent with a simulated user and you can test the thing users actually do.
A user sends you a screenshot of your agent doing something wrong. You have the logs, you have the trace, and you still cannot make it happen again, because the model sampled differently and a tool returned something new. You are debugging a ghost. Record every source of nondeterminism during the run and you can replay that exact failure on demand.
You wired an LLM up as the grader for your evals, and now every release looks green. The problem is the judge is not scoring quality, it is scoring length, order, and answers that sound like its own. Fix the three biases that matter and calibrate the judge against human labels, so the number your pipeline gates on actually means what you think it means.
A tool call times out, your agent retries it, and the customer gets billed twice because the first request went through before the response came back. The fix is not fewer retries. It is idempotency keys on every write tool, so a repeated call with the same key returns the first result instead of doing the work again.
A user closes the tab and your agent keeps going: three tool calls in flight, two subagents still reasoning, tokens still burning for an answer nobody will read. The fix is a deadline that every layer respects and a cancel that propagates down the whole tree, tearing in-flight work down cleanly instead of leaving it to finish alone.
Most of what an agent does is safe to let run. A few things are not: the refund, the production deploy, the email to a customer. The answer is not to make the agent slower everywhere. It is an approval gate that pauses the run before the risky action, persists the pending decision, and resumes exactly where it stopped once a human says yes or no.
Input defenses stop bad instructions from getting in. They do nothing about what your agent says on the way out. One generated reply that leaks another customer's data or makes a promise you never authorized is a message you cannot unsend. A fail-closed egress layer that checks every output before it ships is how you keep that message from ever leaving.
LLM spend is per request and wildly variable, so a single runaway agent or one heavy tenant can externalize its cost straight onto your margin. Cheaper models and caching lower the average, but nothing stops the bill. A per-tenant spend ledger with the cap enforced before the call does.
In a long session an agent keeps every tool result it ever saw, including the three older versions of a file that has changed twice since. It then acts on the wrong one. This is agent drift, and it is a correctness bug, not a token bill. Here is how staleness-aware pruning fixes it.
An agent that spawns subtasks and fans out tool calls builds an unbounded work queue, hammers your rate limits, and burns the token budget before the run finishes. Backpressure, bounded concurrency, and circuit breakers keep the fan-out from taking down the systems it depends on.
A long agent run that crashes halfway restarts from scratch, re-spending every token it already paid for and re-firing side effects you never wanted twice. Durable execution fixes that with checkpoints, replay-safe steps, and idempotency keys.
Once an LLM feeds a real workflow, "mostly valid" JSON is a failed handoff, not a formatting quirk. Here is how to get schema-valid output every time with a validation gateway that parses, repairs, and fails loudly.
Most agent failures in production are not bad reasoning, they are bad tool calls: wrong arguments, expired tokens, timeouts. Here is how to validate, classify, and recover from tool failures instead of retrying blindly.
A single LLM handling routing, retrieval, reasoning, and execution is easy to prototype and brittle in production. Here is when to break a monolithic agent into an orchestrator-worker architecture, and when not to.
Most teams find out a prompt change broke something from a support ticket, not from CI. Here is how to build an eval gate that scores your LLM outputs and blocks regressions before they ship.