Introducing AI Coding Agents: A 100-Day Plan

Most AI rollouts in dev teams follow the same script: licenses are bought, a workshop happens, three enthusiasts use the tools daily, everyone else falls back into old habits, and after six months nobody can say what actually changed. The mistake is treating agentic coding as a tool rollout. It is a change of working model, and working models change through practice, structure and measurement. Here is the plan we run with teams, compressed to its spine.

Days 1–20: Baseline and verification

Two things before any agent writes production code. First, measure the starting point: cycle time and throughput, honestly. Without a baseline, the day-80 conversation is opinions. Second, close the verification loop: tests the team trusts, running in CI, fast enough to iterate against. This is the single highest-leverage step in the whole plan: an agent that can check its own work iterates without a human in the loop; an agent that cannot stops when the code merely looks done. If test coverage is thin, the first agent tasks are writing tests: low risk, immediate value, and it builds the loop everything else depends on.

Days 21–60: Real tasks, adapted process

No sandbox exercises. Agents work the real backlog (bug fixes, refactorings, well-scoped features) with developers pairing on orchestration: describing tasks precisely, setting constraints, reviewing results. Three process changes happen in this window, deliberately. Review adapts: when an agent produces 500 lines in minutes, review shifts from line-by-line reading toward architecture, contracts and test quality, with clear rules for what needs human eyes. Mistakes become memory: every repeated agent error becomes a rule in a persistent file (CLAUDE.md, rules), so the setup gets smarter weekly. Parallelism starts small: one developer, two agents on git worktrees, then wider as review keeps pace.

Days 61–100: Scale, measure, hand over

Now the toolbox spreads across the team, and repeated workflows get encoded into durable artifacts (commands, checklists, sub-agents) so the gains stop depending on individual enthusiasts. At day 80, measurement: cycle time and throughput against the day-1 baseline. Numbers, not impressions. If they have not moved, something in the setup is wrong, and twenty days remain to find it. The final stretch is knowledge transfer by design: the team runs the setup without external help, or the introduction has failed regardless of the metrics.

The failure modes to avoid

We see four repeatedly: buying licenses without changing the review process (a few percent gain, then plateau); starting with the hardest legacy module instead of building the verification loop first; letting each developer improvise a private workflow instead of standardizing the five practices; and declaring victory on enthusiasm instead of measuring at day 80. Every one of these is avoidable with the structure above.

This is precisely the shape of our engagements: we embed in the team, run this plan with your real backlog, and leave at day 100 with the numbers on the table and the capability in your team; see AI Applications and the FAQ.