Context
Cognition is considering rolling out a new Devin-powered workflow that can read internal runbooks, propose code changes, and open draft pull requests for routine engineering tasks. As an Engineering Manager, you need a practical framework to decide whether a team is actually ready to adopt this LLM workflow safely and effectively.
Constraints
- p95 end-to-end workflow latency: under 90 seconds for a standard task
- Cost ceiling: under $8 per completed task at 5,000 tasks/month
- Hallucination ceiling: fewer than 2% of accepted PRs may contain unsupported or fabricated changes
- Prompt-injection success rate: under 1% on adversarial internal docs and issue threads
- Human override required for any production-impacting action
- The workflow must respect repo permissions and avoid exposing secrets or sensitive code outside approved boundaries
Available Resources
- 12 months of historical engineering tasks: tickets, diffs, reviewer comments, CI outcomes, and rollback incidents
- Internal documentation in Notion, GitHub, and incident runbooks
- Devin, a smaller low-cost model for triage/classification, and a stronger frontier model for code reasoning
- Existing observability on task duration, review cycles, CI pass rate, and incident metrics
- 20 senior engineers available to label a golden set of tasks and failure cases
Task
- Define an evaluation-first readiness framework for deciding whether a specific engineering team should adopt this Devin workflow.
- Specify the offline and online evaluation plan, including how you would measure task success, hallucination risk, prompt-injection resistance, and human-review burden.
- Design the high-level agent architecture, including where retrieval, planning, tool use, and approval gates should sit.
- Propose a rollout strategy that segments teams by readiness, and explain what signals would block launch or trigger rollback.
- Estimate cost and latency tradeoffs, and identify the main failure modes and mitigations.
