AI reliability, from production to outcome
Find the failures your dashboards miss. Investigate them in business context. Test the fix across the tools your agent uses. Verify the customer’s result.
Tensile / Customer job R-104
Intent
One approved request → one saved record
Observed effect
Write committed → response lost → second insert
Tensile finding
Reconcile the request identity before any retry
Illustrative records · proposed experience
For teams already putting agents into real workflows. Tensile connects the customer’s expectation to the execution, the tools it changes and the final result.
01
Question, evidence, cause and owner decision.
02
Reusable checks, realistic environments and a tested revision.
03
The saved effect, customer outcome and lesson for the next release.
Duplicate records after a tool timeout
Two records share request R-104. The agent retried after losing the write response.
Clickable product concept · invented records · no external execution.
A closer look / illustrative case
A request travels from Slack to email context, Teams approval and a database. The write succeeds. Its response does not. A retry creates a duplicate. The answer sounds fine; the work is wrong.
Intent
One approved request → one saved record
Observed effect
Write committed → response lost → second insert
Tensile finding
Reconcile the request identity before any retry
Tested condition
Repeat the timeout; check saved state and every attempted write
Customer result
Confirm the single authoritative record and intended owner
Illustrative reconstruction. A passing replay supports the tested fix; production outcome evidence must still follow.
01 / Find what needs attention
Bring agent executions, customer feedback and business records into the same investigation. Follow a recurring problem back to the affected work, with source coverage and freshness visible.
— Find repeated failures and critical exceptions
— Distinguish an agent error from a failed tool or missing evidence
— See which customers, jobs and versions need attention
Explore production intelligence ↗
02 / Investigate in context
Why did the agent file the document twice? Why did it ignore a reviewer’s decision? Investigate combines execution evidence with your repository, policies and documents to examine what should have happened and what did.
— A versioned investigation skill for the question
— Expected and observed journeys with evidence at the gap
— Chat corrections, decisions and reruns that retain their history
Inside Investigate ↗
03 / Improve the real workflow
Slack, email, Teams, databases and the handoffs between them. Recreate the relevant state, identity and permissions. Test ordinary work alongside timeouts, stale records and denied access. Inspect the effects independently.
— Equivalent starting state for baseline and candidate
— Versioned checks for the task, policy and actual effects
— Evidence for a reviewed code, prompt or configuration change
Explore environments ↗
04 / Verify the customer outcome
A tool returned successfully. An evaluation passed. A change shipped. Each tells you something different. Follow the customer’s work across agents and handoffs to the saved decision, completed transaction or correct delivery.
— Completed, incomplete, harmful failure or unable to determine
— Link later corrections and recovery to the original work
— Measure human review effort and consequential misses from real sources
See outcome verification ↗
Built from the hard cases
A missing obligation never entered the review queue. A cited financial answer used the wrong denominator. A second email split one customer task into two. Read the failure mechanisms and the product decisions they inform.
Whole-file coverage comes before finding accuracy. ↗
Citations cannot verify a calculation. ↗
Email identity is not work identity. ↗
Read all eight field notes ↗
Start with an affected customer job, the expected result and the evidence your team would accept.
Discuss a concrete workflow ↗
AI reliability, from production to customer outcome.