AI transformation · Engineering operating model
Governing agentic delivery at BigHand
Turning coding-agent ambition into a controlled engineering system that teams can trust, inspect, and improve.
BigHand
Software Engineering Manager · governance and guardrails leadership
Design direction
A practical governance direction for AI-assisted delivery—connecting autonomy with validation, escalation, and audit-ready evidence.
How the work connects
- Define the work
- Constrain execution
- Validate independently
- Escalate and retain evidence
Context
As engineering teams moved from AI assistance toward agentic delivery, the opportunity expanded beyond developer productivity. The operating model needed to cover how agents receive work, where they execute, how their output is validated, and what happens when judgment is required.
Challenge
Create enough control to make agentic delivery dependable without removing the speed and leverage that make it valuable.
Approach
Make the system work.
- 01
Frame governance across the complete lifecycle: planning, execution, validation, and evidence.
- 02
Define controlled environments and observable checkpoints for agent work.
- 03
Build exception reporting and decision escalation into the normal flow of delivery.
- 04
Treat evidence capture as a product capability, not an after-the-fact documentation task.
Impact
What the work covers.
- The design covers planning, execution, validation, escalation, and evidence across the delivery lifecycle.
- Controlled environments and exception reporting are part of the governance scope.
- Validation, captured evidence, and human decisions are being considered together within the proof of concept.
Disciplines
- AI adoption
- Agentic SDLC
- Governance
- Engineering leadership
Contact
Start a conversation.
If my work is relevant to something you are building, I would be glad to hear from you.