AI Agent Consulting
Production AI agents that act in your tools under scoped permissions — not chatbots, not RPA scripts, and not a strategy deck.
- Service
- AI Agent
- Industry
- Enterprise
- Updated
- 2026-08-25
- Engagement
- 4 wks
AI agent consulting designs, builds, and hands over production agents that read context, call scoped tools, and change state in systems you already run — with an eval suite, shadow mode before write access, and a human fallback. It is not chatbot consulting and not RPA. A standard engagement is four weeks inside your cloud, and you own the weights, datasets, eval suite, and runbook.
Why teams pick this engagement
AI Agent × EnterpriseScoped tools, not open access
Agents act only through the tools you allowlist. Destructive and irreversible calls sit behind confirmation until the eval suite holds on real traffic.
Eval suite before write access
Shadow mode scores the agent against your golden cases. Write permissions open after quality holds — not after a demo transcript looks good.
Your stack, your perimeter
Work runs in your cloud against your identity provider and data stores. Model-agnostic: Anthropic, OpenAI, Google, Mistral, or your fine-tunes.
Human fallback is design
Low-confidence and high-stakes cases route to a person with the trace attached. Failures become new eval cases so the same miss is caught next time.
Four-week standard
Discovery week one, environments week two, shadow-mode pilot week three, handover week four. One well-bounded workflow with a numeric success metric.
You own the result
Weights, datasets, eval suites, and runbooks are yours at handover. You pay the model provider directly — we add no token markup.
Key takeaways
- 01
A chatbot produces a reply. An AI agent retrieves context, calls tools, and changes state in systems you already run — under permissions you issue.
- 02
RPA follows a flowchart. Agents handle messy language and long-tail cases; that flexibility is the risk unless evaluation and fallback are built in.
- 03
Start with one high-volume, well-bounded workflow and a numeric success metric. One live, measured agent beats five half-built ones.
- 04
Read-only and shadow mode come first. Write actions open only after the eval suite holds on real traffic, not on a demo.
- 05
You own the result. Work runs in your perimeter, models are yours to choose, and inference is billed by your provider with no token markup from us.
What the engagement covers
How we work
- 01
Discover
Week one: one workflow, one metric, systems audit, golden cases, and a written scope.
- 02
Design
Tool allowlists, fallback rules, eval plan, and architecture reviewed before a line of production traffic.
- 03
Build
Agent, tools, traces, and environments inside your cloud with weekly demos.
- 04
Validate
Shadow mode on live traffic scored against the eval suite; write access stays off until quality holds.
- 05
Enable
Handover of weights, evals, and runbooks; your team operates it with 30 days on-call.
Take the playbook with you
The working documents from real engagements — free, in exchange for an email. They’re useful whether or not we ever talk.
AI Agent vs Chatbot vs RPA Decision Sheet
A one-page rubric for picking the right pattern: script, copilot, or acting agent — with the failure modes each one creates when you pick wrong.
Get the decision sheet ·First-Agent Workflow Scoring Worksheet
Score candidate workflows on volume, boundedness, tool access, and reversibility — the same filter we use before week one.
Get the worksheet ·