Top AI Tools 2025: Ship Faster With the Right Stack

The AI stack in 2025 is crowded, but the winners share a pattern: they shorten pull request cycles, keep risks contained, and integrate cleanly with GitHub. This guide spotlights the tools that consistently help engineering teams ship faster across review, IDE workflows, testing, observability, and research. We also cover how new assistants like Google Antigravity fit into the workflow as a research and summarization layer, plus link to deeper playbooks on automated review and AI testing.

Selection criteria that matter in 2025

We score AI tools on five signals: integration depth with GitHub and CI, control of data residency, explainability of suggestions, measurable latency, and how easily teams can tune policies. These criteria reduce the risk of vendor sprawl and fragmented audits.

Research assistants and knowledge discovery

Google Antigravity and Perplexity help teams synthesize RFCs, tickets, and prior incident reports. Antigravity shines when you need multi-source summaries with citations. Keep these assistants scoped to non-sensitive knowledge or use organization policies to restrict data sharing.

Use research tools to create context packs for PR reviewers, but avoid pasting sensitive code. Instead, point assistants to public docs and sanitized architecture notes.

Code review and quality automation

Propel Code delivers contextual AI review, policy automation, and reviewer analytics inside GitHub checks. Pair it with secret scanning and SCA so every PR is covered. Keep a baseline of deterministic rules for security and compliance while AI handles reasoning-heavy feedback. For a deeper comparison of AI reviewers, see our showdown guide.

For regulated teams, ensure the reviewer produces evidence you can export. Propel's policy enforcement and knowledge base let you memorialize guardrails so they are enforced on every change.

AI IDEs and coding assistants

Cursor and Windsurf lead for repo-aware editing with local context indexing. VS Code with Copilot remains the broad default, while JetBrains AI appeals to polyglot teams invested in IntelliJ. Use these tools for refactors, test generation, and navigating large codebases.

When pairing an AI IDE with a research assistant like Antigravity, keep boundaries clear: IDEs work on code, research tools summarize product context and tickets. Avoid blending secrets across the two.

Testing and QA

Playwright, Vitest, and Jest now ship AI-assisted codegen that drafts tests. For regression safety, add contract tests and snapshot diffing. Use AI suggestions as scaffolding, then harden assertions manually.

In CI, gate merges on test pass plus an AI review signal. Track median time to green to see whether automation speeds up or slows down delivery.

Security and compliance

Combine code scanning (CodeQL or Semgrep), secret detection, and SBOM generation with AI triage. AI should summarize risk and propose fixes, but the authoritative signal remains deterministic scanners. Record evidence for audits.

For supplier reviews, document how tools handle data: does context leave your VPC, and is training disabled on your prompts? Keep a short trust report for every vendor.

Observability and ops

Sentry AI, Datadog AI, and New Relic Grok help turn traces into action items. Feed the resulting summaries back into PRs so engineers see production context while coding. Keep AI-generated runbooks under version control to avoid drift. For async team patterns, review distributed review practices.

How to choose by team size

• Early teams: Pick one AI IDE, Propel for PR review, and a single research tool like Antigravity. Keep the stack simple.
• Growing teams: Add dedicated testing helpers and formal policy packs in Propel. Track PR cycle time and review latency.
• Enterprises: Enforce data residency, SSO, audit exports, and least privilege across every tool. Standardize prompts and knowledge bases.

Evaluation scorecard before you buy

• Data handling: residency, training off by default, audit exports.
• Context depth: repo indexing, knowledge base support, maximum token budget.
• Latency: p95 under 5 seconds for review comments and under 1 second for IDE completions.
• Explainability: diffs plus rationale, links to policies, and surfaced evidence.
• Admin controls: SSO, SCIM, role-based access, rate limiting, and org-wide prompt libraries.
• Integration coverage: GitHub App checks, CI annotations, and webhooks for enforcement.

90-day rollout plan

1. Week 1-2: Pilot on two low-risk services, collect qualitative feedback and latency stats.
2. Week 3-4: Enable Propel policy packs plus secret scanning across all repos; measure PR cycle time.
3. Week 5-8: Add AI IDEs for a volunteer squad; require CI plus human review on all AI changes.
4. Week 9-10: Introduce Antigravity for research on public docs; codify what data is in-scope.
5. Week 11-12: Publish operating playbook, enable SSO/SCIM, and review audit logs with security.

KPIs to watch

• Median PR cycle time and review latency (should trend down without more escaped defects).
• Acceptance rate of AI suggestions in PRs and IDEs (signal-to-noise proxy).
• Flaky test rate and rerun counts after AI-assisted edits.
• Security findings per KLOC before vs after AI adoption.
• Cost per developer per month for AI tools versus time saved on PRs and incidents.

Governance checklist

• Document which tools can access which repos; restrict private data in research tools.
• Disable training and enable retention limits where available.
• Log all AI actions in PRs; export reports monthly for audits.
• Keep deterministic scanners (SCA, secrets, code scanning) as blockers in CI.
• Review prompts and knowledge bases quarterly to remove stale guidance.

FAQ

How do I prevent AI tools from leaking secrets?

Disable training, scope org policies, and keep a secrets baseline in CI. Avoid pasting tokens into assistants and prefer local context indexing.

Where does Antigravity fit?

Use Antigravity for research, competitive scans, and summarizing tickets. Do not expose proprietary code or credentials; treat it as a research layer, not a code executor.

Ready to pair AI review with your GitHub workflow? Try Propel in your next pull request and keep policy enforcement predictable while velocity climbs.

Sources and further reading

• NIST Secure Software Development Framework (SSDF) for aligning AI tooling with secure SDLC practices.
• GitHub code security docs covering secret scanning, SCA, and code scanning workflows.
• OWASP ASVS as a reference for policy checks and secure coding requirements.