Reliability for AI agents in production
Your agent’s reliability, measured.
Tensile Reliability reads what your agents actually do in production, including the failures your evals never caught, and turns it into a reliability scorecard you can act on. It reads agents on any model or framework, including LangGraph, Bedrock, voice and chat, whether your team, a vendor or we built them.
The problem
Your agent is failing customers. You can’t see it.
Agents don’t crash when they fail. They give a confident wrong answer, skip a tool, or talk a customer in circles, and move on. Uptime is green, the evals pass, and nobody owns the gap between the service being up and the agent doing right by the customer.
01
The failures are silent
The agent tells a customer their claim was approved without ever calling the policy API. Nothing errors, no test catches it, and you find out from an angry thread days later.
02
Your most expensive engineer is the first responder
When something breaks, the first person to read the trace is the one engineer who can. Support can’t triage it, so every incident lands on the person who can least afford the interruption.
03
No number says it is improving
Aggregate success looks fine while individual customers quietly churn. Without one figure that moves, you can’t tell whether any fix worked.
The reliability loop
Score it. Triage it. Enforce it.
Reliability scorecard
Your agent’s reliability on one page.
Task success, escalation rate, resolution rate, tool-call success and time to resolve, computed from your real traffic and filterable by customer. The numbers you defend a budget with.
Every signal in one place, per customer and per agent
Hard facts first, such as tool errors and escalations, so the numbers hold from day one
Watch the number move as you fix things
Customer-lens triage
See which customer got burned, and why
Failures surface soon after they happen, including the silent ones no customer reported, keyed to the end customer, ranked by severity, with the full session record attached.
Surfaces failures that never became a ticket
Grouped by the customer who was affected
Jump from a failure straight to the session record
Policy and procedure checks
Turn your procedures into checks that run live
Write how your agent must behave in plain language, such as the disclosure it always makes. Every run is checked against it, and a breach is flagged shortly after it happens.
Write rules in plain language, with no rule language to learn
Every new run is checked
Built for regulated workflows where one breach is costly
Fixes, with a human in charge
A human merges every fix
It can suggest a prompt change and show replay results for a person to review. Autonomy is earned step by step, and a human always merges.
How it works
From connect to your first scorecard.
No golden dataset to write and no eval framework to learn. Point it at one agent and the first scorecard comes from your own traffic.
1. Connect your agent
Point it at your traces through our SDK, an OpenTelemetry export, or the Langfuse connector.
No schema changes, no redeploy
2. Get your scorecard
It reads what your agent actually did and computes the signals.
A scorecard for each customer, with the failures behind it
3. Improve at your pace
Enforce your procedures and, when you are ready, it drafts the fixes.
Observe, then recommend, then apply with your approval
Questions
Frequently asked.
What engineering leads ask before they connect an agent.
01
How is this different from observability?
Observability tells you the service is up, what you spent on tokens, and how fast the agent replied. None of that says whether the agent did right by the customer. This scores that half: task success, escalations, policy breaches, and silent failures.
02
How do you know the scores are right?
The scores start from failures you can already prove, such as tool errors, loops, escalations, and dropped tasks. Subtler ones are calibrated against your own labelled outcomes, and every verdict shows its evidence so you can check it.
03
Isn’t this just evals?
Evals are one part of it. Most eval tools hand you a harness and ask you to write the golden dataset. This mines the evals from your real traffic and folds them into scoring, triage, procedure checks, and the fix loop.
04
Will it change my agent on its own?
No. It drafts the change, replays it against your real history to check it helps without breaking anything, and opens it for review. Suggested prompt changes wait for human review. Autonomy is earned in steps: observe, recommend, then apply with your approval.
05
Does it work with our stack?
Yes. It works with any model or framework: LangGraph, Bedrock, direct model calls, voice, or chat. If your agent produces a trace of steps and tool calls, it can read it, through an SDK, OpenTelemetry, or the Langfuse connector.
06
How is sensitive data handled?
Traces travel over HTTPS. You choose what to send, and you can redact fields in your own application before anything reaches us. Ask us for our data retention and storage details on a call.
07
Can we run more than one agent?
Yes. Teams run several agents side by side. Reliability is scored per agent and rolled up, so you can see where any agent, or the whole fleet, is drifting.
Build and run
Start with one call.
Maybe nothing is built yet, or your agents are already live and failing quietly. Tell us which, and we will show you where to begin.
We build AI agents and keep AI agents reliable, whoever built them.
Tensile AI, formerly TrustEvals
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