§ 03 · Domain mastery

AI Agents & LLM-Ops

Agents that survive contact with a real business — evals, guardrails, cost, and audit.

Boring infrastructure that makes the magic reliable: evaluation harnesses tied to release gates, policy and PII guardrails at the request path, token budgets enforced per feature, and traces you can hand to compliance. We build the layer that separates a demo from production.

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§ I

Pillars

01

Evals as release gates

A test suite of task-shaped prompts, scored on outcomes the business cares about. New model? Rerun the suite; the gate says ship or don't.

02

Guardrails at the request path

PII redaction, jailbreak resistance, per-tool allowlists, and structured-output validation — enforced before the response leaves the process.

03

Token accounting per feature

Every request tagged with feature, user tier, and cost. Attribution to product owners, alerts before invoicing surprises the CFO.

04

Human-in-the-loop by design

Destructive actions require named human approval. Approval is auditable, replayable, and belongs to a person — not a service account.

05

Retrieval that actually retrieves

Chunking strategy, reranking, and eval-driven tuning. Hybrid search + BM25 fallback so the answer isn't at the mercy of an embedding model's mood.

06

Trace + replay for auditors

Full request context — prompt, tools, retrieval, model, output — stored and replayable. When compliance asks 'why did it say that?', you answer.

§ II

Domain vocabulary

The words that separate insiders from readers.

Eval harnessThe test suite that decides whether a new model or prompt ships. Teams without one make deploy decisions on demos.
Prompt injectionThe failure mode where user input hijacks the system prompt. Insiders name it by attack shape, not just 'jailbreak'.
Tool callThe model deciding to invoke a function. Whether tool arguments are trusted decides your blast radius.
Context window rotAs history grows, the model gets worse at attending to it. The failure mode that makes long chats mysteriously go stupid.
RerankerThe second-pass model that reorders retrieval results. Adding one usually beats swapping the embedding model.
Structured outputGrammar-constrained generation. The difference between hoping for JSON and getting JSON.
MCPModel Context Protocol. The connective tissue for tools and data. Correctly used, it kills the bespoke-adapter treadmill.
Golden datasetThe curated set of inputs your evals are graded against. Its quality is the ceiling of your reliability claims.
§ III

Open problems we help with

§ IV

What we ship

  1. 01An eval harness wired to your release pipeline.
  2. 02A guardrail layer that redacts PII and validates tool arguments.
  3. 03Token budgets per feature with alerting before invoice.
  4. 04A trace store you can replay against a new model version.
  5. 05A human-approval workflow for destructive actions.
  6. 06A runbook for the top five failure modes we've already seen.
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