The AI Agent Development Process
We follow a rigorous, evidence-based approach to identifying which AI agent use cases will deliver measurable value to your team. Rather than building agents for their own sake, we surface real operational friction and validate solutions before scaling.
Our Four-Stage Process
Interview Teams & Surface Friction
We conduct interviews with operations, product, and engineering teams to identify repetitive work, error-prone tasks, and time-consuming bottlenecks. We document:
- Current workflows and tools
- Time spent on routine tasks
- Error rates and rework cycles
- Team preferences and constraints
Propose & Assess Feasibility
Based on discovery findings, we propose three candidate use cases ranked by impact and feasibility. Each includes:
- Problem statement and current workflow
- Proposed agent solution
- Success metrics (time saved, error reduction, cost per outcome)
- Implementation effort and blockers
Pilot with Real Teams
We run each candidate use case as a one-week pilot with an internal team. The agent handles real work while we measure:
- Time saved per task
- Error rate (vs. manual baseline)
- Cost per outcome
- Team satisfaction & adoption signals
Document & Spread Adoption
Validated use cases become reusable blueprints: acceptance criteria, cost model, measured outcomes, and playbook for other teams. Success means adoption by a second team with similar evidence.
Why This Approach Works
Evidence-based: We measure impact before scaling. No guessing on ROI.
Team-first: Every use case solves a real operational problem that a team identified, not what we think should be automated.
Scalable: Each validated blueprint can be deployed by other teams without custom engineering.
Low risk: One-week pilots prove value in production before committing resources.
Ready to Start?
Whether you're curious about AI agents or have specific operational challenges, we can help. Let's discuss your team's friction points.
Start a Conversation