Demos don't survive production
A prompt that works on ten hand-picked inputs meets ten thousand real ones. The failures aren't crashes — they're confident, plausible, wrong answers that nobody catches until a customer does.
Agentic AI · RAG · AI harness engineering
AI that survives contact with production.
Most AI projects fail after the demo, not before it. We build the parts that decide whether it holds: retrieval that returns the right context, agents that stop when they should, and the tooling layer around the model — tools, traces, evals, and fallbacks — so you can change something and know whether it helped.
- Evals before opinions
- Traces on every run
- Direct with the engineers
Agent run
Context
Tools
Loop
Traces
Evals
01 retrieval is ranked and cited, not stuffed into a prompt
02 every tool call is typed, scoped, and reversible
03 traces feed the evals that decide the next change
The pattern
The demo worked. Production is a different system.
Retrieval returns the wrong context
The model answers from whatever the retriever handed it. When that's semantically close but factually stale, the output reads perfectly and is still false. Most stacks have no way to tell the difference.
No evals, so nobody knows
Somebody changes a prompt and everybody argues about whether it got better. Without a scored test set and traces to read, every change is a guess and every rollback is a hunch.
Services
Three layers. One team.
Retrieval, agent behavior, and the tooling around the model are one system — a good agent on bad context still fails, and neither is fixable without traces. We build all three so no seam is nobody's job.
Agentic AI
Agents that do bounded work: planning loops with explicit stopping conditions, narrow and typed tool access, human review at the steps that warrant it, and deterministic fallbacks for when the model is wrong. Built to be interrupted, resumed, and audited rather than trusted blindly.
RAG and Retrieval
Retrieval that earns the context window: chunking tuned to your documents, hybrid search with reranking, freshness and permission filters that hold, and citations back to source. We evaluate retrieval on its own — before the generation step — because a good answer over the wrong passage is still wrong.
AI Harness Development
The tooling layer around the model: tool definitions and schemas, orchestration, structured traces for every run, eval suites with scored regression sets, guardrails on input and output, and fallback paths when a provider degrades. This is the part that decides whether you can change the system without breaking it.
Approach
Measure first, then change things.
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Scope
Scope it to something gradeable
We define the task narrowly enough to be scored. Most failing agents were asked to do something nobody had written down.
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Baseline
Baseline what it does now
We build the eval set before touching the system: real inputs, scored outputs, traces on current behavior. Until that exists, every improvement is an opinion.
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Build
Build against the score
We change retrieval, tools, and the loop in increments, and each change is kept or reverted on the numbers. You see the code, the traces, and the reasoning as it lands.
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Run
Run it, then hand it over
We deploy it, watch it, and fix what breaks — then document the harness so your team can run and extend it without us.
Contact
Start with a short technical conversation.
Bring the agent that works in the demo and not in the app, the retrieval stack that answers confidently from the wrong document, or the prompt change nobody can prove helped. We will tell you what it needs.
Thirty minutes if you want to walk through the system in depth. Fifteen if you just want to work out whether we are a fit.