Multiple engagements across our client base
Planning, tool calling, evaluation harnesses, and the audit trail that makes an automated decision defensible.
Clients kept coming to us with the same shape of problem. A process where every step needs judgement: read this document, check these three systems, decide what to do, write the result back. No single step is mechanical enough for a conventional workflow engine. The traditional approach ends up as a hundred nested conditionals that work on the first few examples and break on the fifth. The question we kept being asked: can an agent handle this end to end?
We treat agentic systems as production software. That means an opinion about how they are built, not just what they do.
The agent plans. We decompose a goal into steps up front rather than letting the model improvise, because improvising in production is how an agent spends a thousand dollars in API calls on a single task.
The agent uses real tools. Not a sandbox and not an abstraction layer: scoped access to the actual business systems, with the right permissions, so a decision the agent writes back is the decision that takes effect.
The agent remembers. Context carries across the task and across sessions, because partial context is worse than no context.
Retrieval is tuned per client. The chunking strategy, embedding model and reranker that work on one corpus never survive the next, so we tune them against the client's own documents before anything ships.
Every agent ships with an evaluation harness. A labelled set of real cases, automated scoring, and a regression gate that runs before any prompt change or model upgrade reaches production. If we can't measure a change, we don't ship it.
Every action gets logged as an auditable decision record: what the agent saw, what it chose, why, what it changed. In regulated contexts an unexplainable automated decision is unusable. In unregulated ones it's a landmine waiting to go off.
Cost ceilings and rate controls at the orchestration layer, because a runaway loop should be bounded by design, not by someone noticing the API bill. Human review checkpoints go where the cost of a wrong answer is high, and come out where it isn't.
Delivered as pods and as embedded specialists inside client engineering teams.
Agents running unattended against production systems, with the audit trail to explain every decision and the evaluation harness to prove a change hasn't regressed.
Capabilities used
Tell us about it. We'll tell you honestly whether we're the right team to help ship it.