Your developers are already using AI coding assistants. The question isn't whether to adopt them. It's whether you're adopting them intentionally or by accident.
Across organizations, developers are adopting AI faster than policies are being defined. The tools are powerful and easy to access, so adoption often happens from the bottom up.
That behavior is understandable, but without shared expectations, costs become unpredictable, context windows fill with noise, agents gain more access than intended, code quality varies across teams, and Git history gets harder to trust.
AI tools aren't the chaos. The lack of strategy is.
A Checklist for Adopting AI Assistants
Before you standardize on tools or models, answer these questions to determine your long-term reliability, velocity, and cost structure.
1. Which agents fit your team's workflow and culture?
Map your workflows: Identify which teams are terminal-first, IDE-centric, or hybrid. An assistant that feels seamless in a CLI can feel clumsy in an IDE, and vice versa. See our “Tool Landscape at a Glance” for a list of the top agents below.
Standardize on a small set: Choose one or two preferred assistants per workflow instead of letting every team pick their own stack.
Document your approach: Create an agents.md that defines roles, rules, and allowed tools for each assistant. Without this, every developer interprets “safe use” differently, leading to inconsistent results and unclear accountability.
2. What model mix balances cost and quality?
Mix models intentionally: Pair a reasoning-heavy “lead” model for complex decisions with a cost-efficient “worker” model for routine tasks. This keeps token spend predictable while preserving quality where it matters.
Set simple budget guardrails: Decide on max spend per project, team, or environment. Review it regularly so costs don’t drift.
Stay flexible on models: Keep options open with local models and routing layers (such as OpenRouter) to manage cost, latency, and privacy. Avoid lock-in that makes it hard to optimize as the landscape changes.
3. What does “safe by default” mean in your environment?
Contain the blast radius: Run agents inside containers or sandboxed environments for auditability, access control, and straightforward rollbacks. Safety is not just about protecting secrets; it’s also about limiting what agents can change and being able to reconstruct what happened.
Apply least-privilege access: Grant each assistant only the permissions it truly needs. This limits the impact of misconfiguration or unintended behavior.
4. How will you keep AI behavior predictable over time?
Protect context quality: Define when to reset context (e.g., per ticket, feature, or time window) and when to summarize. Polluted context windows degrade performance, increase token usage, and make behavior less predictable over time.
Isolate responsibilities: Use subagents per feature, service, or domain so one agent’s mistakes don’t cascade into unrelated parts of the system, in either code or context.
5. How will you keep your codebase reviewable?
Treat Git as a safety system: Require atomic commits and readable diffs for all agent-generated code. Your Git history is your source of truth. If it becomes noisy and incoherent, code review and incident response suffer.
Make code review mandatory for AI-authored changes: Set expectations for what reviewers should look for (e.g., logic errors, security implications, style, and convention) so AI-generated code meets the same standard as human-written code.
AI Assistants in Practice
At AI Field Day 7, I walked through how AI coding assistants can help teams ship faster, reduce avoidable risk, and give developers back the focus they’ve lost to context switching and manual work.
If your team is exploring AI in software delivery, start by answering these five questions. You’ll be in a much better position to decide what comes next.
