Earlier service perspective
An earlier approach to this work, retained for readers following our original articles. For the current product, start with a concrete failure and follow the reliability lifecycle.
How teams work with Tensile ↗
About this material
This historical material retains its original perspective. Delivery promises, example metrics, regulatory references and timelines have not been revalidated here. It is not a current offer or a compliance conclusion.
Agents that do the work.
We build AI agents that do real work inside the systems a business already runs, and we keep them reliable once they are live. Build is the first half: scoping the agent, building it, taking it to production and handing it to your team.
What we build.
Agents and orchestration
Agent chains, tool routing, model selection, retry and fallback: the control plane that turns the job into production behavior.
Retrieval and grounding
Retrieval pipelines, chunking, hybrid search, freshness and citation, so the agent answers from your own sources.
Tests and evals
CI-grade evals, regression scenarios and a curated golden dataset, so a regression shows up between releases.
Observability
Trace capture, drift indicators and policy-violation alerts, wired into the stack you already run.
How a build runs.
We start by deciding where the agent earns its place: the work it has to do, what done right means, and the rules it has to follow. A named Tensile AI engineer then embeds with your team, works in your repo, your CI and your stack, and ships in two-week increments with a named outcome for each. At the end we train your team to own the agent and hand it over.
After handover, Run.
Once the agent is live, our reliability platform can keep reading its production traces, find failures and their root cause, and check every run against your written procedures. See Run.
Proof from our builds.
For an AI-native finance software company, we took an FP&A (financial planning and analysis) copilot from about 60% to 95% stated accuracy, about 90% measured, with 90+ regression scenarios gating each release. Read the case study.
For financial analysis, we built an agentic harness over company P&Ls: governed retrieval, multi-model generation and a maker-checker review, behind evals that check accuracy, PII safety and authorization on every run. Read the case study.
Questions buyers actually ask.
When should we bring you in? When you know the work an agent should take on and need it built, or when an agent has to go from prototype to production.
What do we actually get? A running agent in your stack, with its orchestration, retrieval, tests and observability, and a team that knows how to run it.
Do we have to use Run afterwards? No. The agent is yours. Run is there if you want it read continuously once it is live.
Related links
From context to an observable check
Define the job
What exact customer result is expected, for whom and by when?
Capture the input, intended owner, authoritative result and completion condition.
See this in Tensile ↗
Find the affected work
Which jobs failed, and what portion of the source is visible?
Record job IDs, agent versions, time window, exclusions and source freshness.
See this in Tensile ↗
Explain the gap
Where did expected behavior diverge from observed behavior?
Link source evidence to the hypothesis; keep missing evidence and owner decisions explicit.
See this in Tensile ↗
Retain the expectation
What reusable check would catch this failure without rejecting correct work?
Include positive cases, counterexamples, policy scope and an independently reviewed expectation.
See this in Tensile ↗
Bring the workflow and the evidence gap.
We can start with an affected customer job and the result your team needs to establish.
Discuss a concrete workflow ↗
AI reliability, from production to customer outcome.