← Solutions
ApplicationTrending

AI agent: pilot to production

Take one high-value workflow from AI demo to a supervised agent running in production, with evaluation, guardrails and a named human owner.

Typical timing
8–12 weeks
Engagement
Fixed scope, then retainer
Delivery framework
User researchDiscoveryAlphaBetaLive
Discuss this solutionApplication capability →

Objective

Prove that an AI agent can do a specific job — triage, drafting, reconciliation, research, customer resolution — to a measured standard, then put it into production behind the same quality gates as any other software. Most pilots stall between demo and production; this package is designed to cross that gap.

You may need this if

  • Several AI pilots, none in production
  • A process with high volume, clear rules and expensive manual effort
  • Leadership wants AI results but risk and compliance teams need controls first
How it runs

Activities, step by step

The plan follows our delivery framework. Steps that do not apply to this kind of work are left out rather than padded.

  1. 01 · User research1 week

    Pick the right job

    • Shadow the people who do the work today
    • Score candidate workflows on value, risk and data readiness
    • Define what "good" looks like with real examples
  2. 02 · Discovery2 weeks

    Design the agent and its evaluation

    • Build an evaluation set from historical cases
    • Design tools, permissions and human approval points
    • Model and platform choice, with cost per task estimated
  3. 03 · Alpha3–4 weeks

    Build and evaluate

    • Agent built with retrieval, tool use and MCP integrations
    • Scored against the evaluation set every iteration
    • Shadow mode alongside the human team
  4. 04 · Beta2–3 weeks

    Supervised production

    • Guardrails, audit logging and prompt-injection testing
    • Live with human approval on every consequential action
    • Cost, latency and quality monitored daily
  5. 05 · LiveOngoing

    Scale with confidence

    • Approval thresholds relaxed as evidence builds
    • Drift and regression checks on each model update
    • Next workflow selected from the same scoring

Deliverables

What you keep at the end.

  • Production AI agent integrated with your systems
  • Evaluation harness and baseline scores
  • Guardrail, permission and human-oversight design
  • AI risk assessment aligned to the EU AI Act and ISO/IEC 42001
  • Operating runbook and cost model

Outcomes

What it is built to change.

  • Hours returned to the team, measured against the baseline
  • An agent you can explain to an auditor
  • A repeatable pattern for the next use case

Often combined with

Every engagement starts with a 45-minute working session with the SME who would own the work. No deck.

Discuss AI agent: pilot to production