AGENT-FIRST SDLC IS NOW TABLE STAKES
AI fluency and agent-first workflows are rapidly becoming baseline expectations for engineering teams, with practical adoption steps available today.
AI fluency and agent-first workflows are rapidly becoming baseline expectations for engineering teams, with practical adoption steps available today.
Hiring signals and platform roadmaps show teams without AI-augmented workflows will fall behind on velocity and cost.
Agent patterns shift testing, tooling, and governance, impacting reliability and compliance if not planned.
-
terminal
Pilot an agent-first path on a real backend ticket (agent writes tests and impl) with cost/time/defect metrics and rollback plan.
-
terminal
Wrap one internal service as a CLI/API for agent control and measure end-to-end lead time and PR review load changes.
Legacy codebase integration strategies...
- 01.
Expose legacy tools via CLI/MCP-style adapters, add permissioning and audit logs, and phase agents into non-critical services first.
- 02.
Harden CI with fast, flaky-resistant test suites and static checks to gate AI-generated diffs.
Fresh architecture paradigms...
- 01.
Start with an agent-first template: AGENTS.md, shared skills directory, clear module boundaries, and sub-10s test loops.
- 02.
Select an agent-capable platform early (e.g., Replit or Codex-backed stack) and standardize prompts, skills, and telemetry.