AI Coding Assistants: Complete 2025 Guide for Engineering Teams

AI coding assistants are now a standard part of the toolchain, but they only improve outcomes when rollout is intentional. This 2025 guide explains use cases, evaluation criteria, and governance so you can add assistants for PR review, refactors, and testing without creating compliance debt. Anchor enforcement with Propel Code so suggestions and PR outcomes stay aligned to your policies. We also cover how research tools like Google Antigravity can support design and discovery without touching code, with references to AI review strategy and AI test generation.

Core use cases

• Pull request review and policy enforcement
• Refactoring and migration support
• Test generation and gap detection
• Docstrings, comments, and changelog summaries
• Research and discovery through tools like Antigravity

Evaluation criteria

Score tools on context depth, governance, latency, and explainability. Require SSO, audit logs, and the ability to disable training. For GitHub teams, check how the assistant posts comments, what evidence it stores, and whether it respects branch protections.

Rollout playbook

1. Define allowed data and repos plus a policy doc.
2. Run a two-week pilot on low-risk services with Propel enforcing review policies.
3. Collect metrics and human feedback; tune prompts and rules.
4. Expand to critical services after security sign-off.
5. Revisit quarterly with audit reports and KPI reviews.

Governance essentials

Keep a register of assistants, scopes, and owners. Enable least privilege and rotate tokens. Require AI output to pass CI and human review. For research assistants like Antigravity, restrict to public or sanitized sources and log usage.

Measuring impact

Track PR cycle time, review latency, merge success, and escaped defects. Monitor how many AI suggestions are accepted versus discarded to gauge signal quality.

FAQ

How do we prevent sensitive data leaks?

Disable training, apply DLP, and restrict which repos can send context. Keep assistants on a need-to-know basis and prefer local indexing where possible.

What KPIs prove value?

Watch PR cycle time, review latency, median time to revert, and escaped defects. If these improve and noise stays low, the assistant is helping.

Want an assistant that enforces your standards automatically? Add Propel to GitHub and ship with confidence while your team experiments with AI helpers.

Policy pack essentials

• Security: secrets, authentication, authorization, and logging checks per repo.
• Quality: test expectations by layer (unit, integration, E2E) and coverage thresholds.
• Architecture: approved libraries, patterns to avoid, and migration guides.
• Compliance: data handling rules (PII, PCI, HIPAA) and retention requirements.
• Documentation: docstring standards and changelog expectations for user-facing changes.

RACI for assistant adoption

• Responsible: engineering leads for each repo roll out and metrics collection.
• Accountable: platform or DevEx for configuration, access, and uptime.
• Consulted: security and legal for data sharing, retention, and vendor review.
• Informed: IC engineers, support, and incident responders on behavior changes.

Baseline-and-measure playbook

1. Capture pre-adoption baselines: PR cycle time, review latency, escaped defects.
2. Pilot with a single squad; tag AI suggestions in PR descriptions for traceability.
3. Compare acceptance rates and defect trends after two weeks; tune prompts and policies.
4. Roll out to more repos with SSO, SCIM, and audit logging enabled.
5. Report monthly to security and platform on usage, wins, and issues.

Sources and further reading

• NIST Secure Software Development Framework for aligning assistant rollout with SDLC controls.
• OWASP Proactive Controls to anchor policy packs and code review criteria.
• Dependabot security updates as a deterministic layer alongside AI assistance.