AI Agent Workflows
Most AI pilots die between demo and production. We scope agents to a workflow with a measurable outcome, build the retrieval and tool layer around it, and ship with evaluations, human review and cost ceilings in place.
What you walk away with
- Evaluation suite gating every prompt and model change
- Human-in-the-loop where the cost of error is high
- Per-task cost and latency tracked in production
What the work includes
Agent design
Tool definitions, planning loops and state handling scoped to one workflow that has an owner and a metric.
Retrieval
Document pipelines, chunking strategy, hybrid search and grounded citations so answers are checkable.
Evaluation
Golden datasets, LLM-as-judge with human calibration, and regression gates wired into CI.
Guardrails
Permission scoping, output validation, audit trails and spend caps enforced at the gateway.
Commercials
How pricing works
Agents drift as your data, prompts and models change. The fixed build gets one into production; the subscription is what keeps it accurate once it is there.
One-time fixed
Built and handed over
Discovery through to a production agent: the evaluation set, retrieval and tool layer, guardrails and human review flow, all running on your infrastructure and documented for your team.
- Workflow scoped with a named metric
- Evaluation suite built from real cases
- Production agent with guardrails and audit logging
- Runs on your infrastructure, fully handed over
Subscription
Kept accurate
Someone watching the evals, migrating prompts when a better model lands, tuning cost and latency, and updating guardrails as the workflow changes shape.
- Eval maintenance and regression watch
- Prompt and model migrations
- Cost and latency tuning
- Guardrail and policy updates
Model and inference costs are billed to your own provider account, so spend stays visible and capped by you.
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Describe the outcome you are after. We will tell you which practice fits, or that none of them do, within 24-48 hours.