Traced
Every run reconstructable — inputs, context, tool calls, tokens, latency
Gated
Evals run in CI; a regression blocks the merge, not the customer
Costed
Spend attributed per task and per tenant, not per API key per month
Auditable
Retention-controlled, redacted logs an auditor can actually sample
Agents do not fail loudly. They degrade quietly, and nobody can say when.
The pattern is consistent. A team ships an agent, it works, and for a few weeks everyone is delighted. Then a model version is deprecated and you migrate. Someone tightens the system prompt to fix one loud complaint. The retrieval index is rebuilt with a new chunking strategy. An upstream API starts returning an extra field. None of these throw an exception — the service keeps returning 200s and the dashboard stays green while answers on the long tail get worse. You find out from a customer, weeks after the change that caused it, and by then nobody can say which change it was because there is no record of what the system actually did on any given request.
The root cause is that prompt changes are deploys with no tests. A one-line edit to a system prompt alters behaviour across every request path in the product, and it usually ships with less review than a CSS tweak. The standard defence is that you cannot unit test a language model, which is true and beside the point. You cannot assert one exact string, but you can score a fixed set of real cases, and you can refuse to merge when the score drops. Without that gate, the only regression detector in your system is a user annoyed enough to write in.
Cost degrades the same way — invisibly. Retries, a context window that quietly grew, a tool loop that now takes eleven steps where it took four: the invoice climbs and nobody can attribute it, because spend is measured per API key per month rather than per task, per tenant or per customer. Underneath both problems sits the compliance question. SOC 2 asks who did what, on whose authority, and whether you can prove it. An agent acting on behalf of a user is an actor in that record. If you cannot reconstruct a specific decision months later — inputs, retrieved context, model and prompt version, tool calls, output, approver — that is an audit finding waiting to happen. None of this is exotic. It is the discipline you already apply to every other production service, applied to the part of the stack that currently has none.
Core Capabilities
What LLMOps tooling actually covers
These platforms overlap more than their marketing suggests. Here is where they genuinely differ — and where the difference matters if you have a compliance obligation.
All three are credible and we work with each; the feature lists converge every quarter, so check current docs before you commit. In practice the decision rarely comes down to features — it comes down to where trace data is allowed to live and whether self-hosting is mandatory. Answer those two first and the shortlist picks itself. And if you are pre-launch with one prompt and ten users, you do not need any of this yet: add basic tracing, ship, and come back when changes start to scare you.
Our Process
Trace What You Already Have
Instrument the existing system before changing anything, then read a week of real traffic. Where the latency goes, where the tokens go, where it silently fails. Most teams are surprised by at least one of the three.
Build the Eval Set
Turn real failures and representative successes into a versioned dataset with graded expectations. This is the artefact everything else depends on, and it is the step teams try to skip. Building the harness is easy; agreeing on what a correct answer looks like is the work.
Gate the Pipeline
Wire evals into CI so prompt, tool and model changes are scored before merge. Quality thresholds, a cost-per-task budget, required review on the files that change behaviour, and a rollback path that has been exercised rather than assumed.
Cost & Compliance Instrumentation
Attribute spend to tasks and tenants, apply redaction and retention policies to the trace store, and map the fields you log to the specific SOC 2 criteria your auditor will test. Done once, before the audit window, not during it.
Hand Over & Monitor
Dashboards, alert thresholds, a runbook, and a written definition of what degraded means for your system so the on-call engineer is not guessing at 2am. You own the code, the datasets and the data.
Tech Stack
We choose the right tool for the job — not the trendiest one.
LangChain
Python
