Help introducing AI-assisted and agentic development workflows while keeping architectural clarity, traceability, review, and operational control.
When This Helps
This work is useful when a team already writes software with AI support, or wants to introduce agents into delivery work, and the surrounding engineering system needs to stay understandable.
- Agent output needs to be tied to specs, issues, commits, reviews, and production behavior.
- Leaders need a practical policy for where agents may act and where human judgment is required.
- The team wants faster implementation loops with code review, architecture review, and test evidence still intact.
- Compliance, auditability, or regulated workflows require a record of intent and decisions.
- Existing developer workflows need clearer handoffs between people, tools, CI, and deployed systems.
What the Work Can Include
Workflow Map
A map of how requirements, specs, agent sessions, commits, reviews, CI, deployment, and operational feedback connect.
Spec-Driven Delivery
Practical structure for turning intent into reviewable work items, prompts, acceptance checks, and implementation traces.
Agent Boundaries
Rules for where agents can draft, edit, test, inspect, or deploy, and where experienced humans must keep ownership.
Traceability and Review
Lightweight evidence around what changed, why it changed, how it was verified, and what remains risky.
Tooling Integration
Connection to existing GitHub, CI, backlog, documentation, incident, and deployment flows where that makes the work clearer.
Team Coaching
Coaching for engineers and leads learning when to delegate to agents, when to read code directly, and how to review AI-shaped changes.
Relevant Background
Recent work includes Spec-Driven Development and agentic delivery in regulated finance, internal HappiHacking tooling for agent-assisted workflows and traceable operations, AI-assisted workflow automation in a pharmacy and e-commerce setting, and compiler/runtime infrastructure work around ML systems at SambaNova.
The point is practical engineering control. The work should help a team see what the agent did, why it did it, how it was checked, and where the next human decision belongs.
Scope
Scope depends on the current workflow, risk level, codebase access, regulatory constraints, and the team's review habits. It can be a short advisory review, a workshop for engineering leads, or a longer hands-on thread around a real delivery process.
