An agent is a model plus a harness, and almost none of the hard parts live in the model. The harness decides what actually goes into the context window, dispatches tools and repairs their output, keeps state across a crash or a restart, enforces what the agent is allowed to touch, and hands off to a human when it should. It is also where the failures live: a model that fabricates a tool call, or reports a task finished when nothing happened, is a harness problem, not a prompt problem. We build that layer, and the eval rig that proves a change to it is safe to ship.
01 / HARNESS
Agent = model + harness. We build the harness: context assembly, tool dispatch, state that survives a crash, permissions, and the eval rig that proves a change is safe to ship.
Usually shipped with
Not a bundle to buy. Whichever you start from, the engagement covers what the build actually needs.
02 / Scope
03 / Outcomes
04 / Deliverables
05 / Stack
06 / Why us
A model that claims a task is done when it is not is the failure mode we wrote about in The Agent Said Done. The Customer Is Still Waiting. Prompt tweaks do not fix it; a verification step in the harness does.
Harness work changes behaviour in ways nobody can eyeball. Every engagement leaves an eval suite in CI, so the next change is measured rather than argued about.
We work in the SDKs and primitives you already run, and leave a layer your team can read. A harness nobody on staff understands is a dependency, not an asset.
Week 1
Before changing anything, we trace a real run end to end: what entered the context, which tools were called, what came back, and where it went wrong. Most harness work is decided by that trace rather than by opinion.
Week 1-3
Assembly and compaction, then schema-enforced tool output with a repair path. These two account for most of the failures that look like the model being unreliable.
Week 2-4
State moves out of the transcript and into a store the run can resume from, so a crash, a timeout, or a deploy does not lose the work.
Week 3-6
The eval harness runs in CI against cases pulled from real traffic. A change to the prompt, the model, the tools, or the harness itself has to clear it before it merges.
Executive operationsIn production
A 24/7 AI chief-of-staff for founders and leadership teams. An agent that plans, acts, and self-corrects across email, calendar, docs, CRM, and ERP through a secure tool layer, runs recurring workflows end to end, and never exposes a credential.
SalesIn production
A real-time meeting-intelligence stack for account executives. It detects a live meeting, transcribes it on-device, guides the rep during the call, and writes a structured, CRM-ready summary the moment it ends, with no audio ever leaving the laptop.
An agent is a model plus a harness, and almost none of the hard parts live in the model. The harness decides what actually reaches the context window, calls the model, repairs what comes back when it is malformed, and resumes from state that survives a restart. Tools dispatch under a schema inside a sandbox that bounds the blast radius, permissions are enforced in code rather than asked for in a prompt, and a verify step checks that a task claimed as finished actually is. An eval harness in CI measures a change to any of it before it merges.
Production-proven
Built by engineers who've already shipped this in production.
Agent development builds the agent as a product: the workflow, the actions, the outcome. Harness engineering builds the runtime underneath it. Teams come to this one when they already have an agent that works in a demo and want the layer that makes it survive real traffic.
Often you need less of it. A framework ships an inner harness that covers the common path; the work is usually in what it does not cover — your context rules, your tool failure modes, your state and permission model. We start by finding which of those the framework already handles.
It overlaps deliberately. LLM evaluation is the standalone engagement when evals are the whole problem. Here the eval harness is one part of the runtime, because a change to context assembly or tool dispatch is exactly the kind of change nobody can review by reading a diff.
That is the usual case. The first week is tracing a real run through what you have, and the plan comes out of that rather than out of a rewrite.
Multi-step agents for workflows where a wrong action is expensive to undo.
View serviceEval suites and regression testing, for teams shipping on opinion instead of a number.
View serviceMCP servers that expose your systems to agents, without handing over credentials.
View serviceMulti-agent systems, for work that outgrew a single agent.
View serviceTell us where you are and what you're trying to ship. We'll come back with a concrete plan, the right engineers, and a path to production, not a generic pitch.