Reimagine code review, test scaffolding and documentation.
Three engineering workflows and the responsibilities people keep.
Code review feedback loop
Responsibilities to review
- Read the diff and surrounding context
- Draft style and correctness comments
- Flag security smells and coverage gaps
- Check reasoning and approve or request changes
Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.
AI reads the diff and the surrounding context, drafts review comments (style, correctness, security smells, test coverage gaps). Reviewer checks the AI's reasoning, adds judgment, approves or requests changes.
Test scaffolding
Responsibilities to review
- Read the new function or module
- Draft happy-path and edge cases
- Set up mocks and fixtures
- Fill in business-logic assertions and review coverage
Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.
AI reads the new function or module, generates the test file with happy path, edge cases, mocks, and fixtures. Developer fills in business-logic-specific assertions and reviews coverage.
Documentation and onboarding
Responsibilities to review
- Read the codebase for the module
- Draft module docs
- Draft API references and ADRs
- Review accuracy and add the 'why'
Illustrative workflow responsibilities. Confirm the tools, data, human decisions and review criteria for your work.
AI reads the codebase, drafts module docs, API references, and ADRs. Developer reviews accuracy and adds the 'why' that the code itself cannot express.
Prepare your workforce for the redesigned work.
Your workforce brings people and AI agents together. Workflows define how they share the work. Define what agents do, who reviews their output, and when they hand work back. Use Task Intelligence to understand the tasks within each step, then prepare your people to build and run the new version through simulations, hands-on projects and skill validation. Revisit affected responsibilities and readiness when the work changes.
Task evidence within engineering workflows
These research inputs include vendor and source-reported software capabilities, product features and descriptions of work. The stored classifications in this selection contain 243 source examples for AI support and 740 for automation. They are not validated workflow outcomes or proof of agent performance. Task Intelligence helps examine the work in context; review each example against your responsibilities, controls and exceptions before using it in a redesign. The first 5 AI-support examples are below.
740 source examples classified for automation›
How we work with engineering orgs
Co-sponsored model. Engineering Manager or Director plus your VP Engineering or CTO in the room together. The 6 to 8 week AI Bootcamp prepares people for agreed workflow responsibilities through simulations and assessments. The Transformation Engagement scales it across the engineering org.
Want an engineering-org-specific walkthrough?
20 minutes. We pull your top three developer task patterns from the dataset and show you the redesign live, with your stack and team mix.
Book a time