This is the engagement when you have a specific workflow and you want a working agent that does it, not a strategy doc about agents.
Three weeks, three phases: scope it tightly, build it cleanly, prove it works against an eval suite. You get a deployed agent, monitoring you can read at 7am, and a runbook for the team that'll own it after we leave.
What we won't do
We won't build a "general-purpose AI agent." Those don't work in production yet. The engagement requires you bring a specific workflow with measurable success criteria.
We won't ship something that's flaky in production. If we can't get the eval suite to pass our quality bar, we extend the timeline (within scope) until we can or we tell you the workflow isn't ready for an agent yet.
We won't pretend an agent is the right answer when a deterministic script is. Sometimes the right tool is sed and a cron job. We'll tell you.
What you can expect at week three
A working agent, integrated with your systems, running on production data, with monitoring you trust. A recorded walkthrough that any team member can watch to understand the agent's behavior. An eval suite that lets you ship updates without breaking the agent. A 60-day support window in case something surprising happens after we leave.
Engagement constraint
We run one of these per month. The build phase is intensive and we don't split focus.
Who this is for
- Operations leaders with a repetitive workflow ripe for AI automation
- Product teams considering an AI agent feature for their core product
- Founders who tried n8n + ChatGPT and want something production-grade
- Companies with 50–500 person teams where automation has measurable ROI
What's included
- Workflow analysis: which workflow, who runs it today, what success looks like
- Architecture: model picks, framework picks, integration paths, observability
- Build: working agent or automation, integrated with your existing systems
- Evaluation: structured eval suite to measure agent quality on your tasks
- Monitoring: dashboards for usage, cost, error rates, and quality
- Handover documentation and a recorded operational walkthrough
- 60-day post-engagement support window
Process
- 01 Workflow scoping (Days 1–3)
Pick the workflow with the team that runs it. Define inputs, outputs, edge cases, and what 'good' looks like. Build a baseline of how it's done today and how long it takes.
- 02 Architecture (Days 4–6)
Pick the model, the framework (LangGraph / Temporal / custom), the integration path, and the observability layer. Write a one-page architecture doc reviewed with your engineering lead.
- 03 Build (Days 7–14)
Implement the agent. Daily progress, weekly demo. We work in your codebase or on a separate service that integrates back, depending on architecture choice.
- 04 Eval (Days 15–17)
Build the structured eval suite. Run the agent against held-out cases. Iterate until quality meets the spec we agreed on.
- 05 Ship & monitor (Days 18–20)
Production deploy, monitoring dashboards, alert thresholds, runbook for the team that'll own it. Recorded handover walkthrough.
- 06 Follow-up window
60 days of support for tuning, edge cases, and questions as the agent runs in production.
Deliverables
- Working agent or automation deployed in your environment
- Architecture documentation
- Structured eval suite with reproducible scoring
- Monitoring dashboards (cost, usage, quality, errors)
- Operations runbook
- Recorded handover session
- 60-day support window
FAQ
- What kinds of workflows have you shipped?
- Customer support triage agents, sales-research agents, document extraction pipelines, internal research assistants, code review automations, content moderation systems, and multi-step approval workflows. We don't ship every kind — see the next FAQ.
- What won't you build?
- Anything that requires a model to make autonomous decisions in a high-stakes context without human review (medical, legal, financial advice). Anything where the workflow is fundamentally vague — agents are bad at fuzzy goals, and we'll tell you so up front.
- Do we need a specific stack?
- No. We build on top of whatever you have — Python, Node, Go, Rust, anything reasonable. We standardize on a small set of frameworks (LangGraph, Temporal, OpenAI Agents SDK, custom orchestration) and pick based on fit.
- What does ongoing operation cost?
- Depends entirely on the workflow's volume and the model used. The architecture doc includes a 12-month cost forecast based on your expected usage. Most agents we ship cost $200–$2,000/month to operate at moderate scale.