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Loading.An agentic SDLC is a software development lifecycle where AI agents carry whole tasks — planning, editing, testing, opening PRs — across the workflow, with developers supervising at the review gates. It differs from copilot-assisted development by how much work the agents own end to end, not by typing speed. Running it reliably takes durable context, observability, and guardrails; without those, an agentic SDLC is a demo, not a process.
Copilot-assisted development keeps the developer doing the work with faster suggestions. An agentic SDLC has agents own whole steps — read the repo, plan, edit, run tests, open a PR — and stop at a human review gate. The lifecycle is the same shape; the question is how much of each stage the agents carry.
Agents are strong at the high-volume, well-specified parts: implementation, tests, refactors, repetitive changes across a codebase. Humans still own judgment calls, unclear requirements, architecture, and the final approval. An honest agentic SDLC draws that line clearly instead of pretending the agents decide everything.
Three layers, the same ones every team hits: durable context so agents can navigate your repo and docs, observability so you can see what they did, and guardrails (scoped permissions, review gates) your seniors trust. Close those and the lifecycle is repeatable; skip them and you get an impressive demo that breaks on your real codebase.
We run an agentic SDLC on our own products — the open suite (WRAI.TH for orchestration, trovex for context, yoru for observability) is the proof. We'd build the same operating layer into your environment and train your developers to run it, on your standards, until they don't need us.
or have us build it — same capability, the other door