Last updated June 2026 · Based on public documentation

AI engineering tools,
compared honestly.

There are excellent tools in this space. Each makes different trade-offs. This page breaks down what each tool actually does, who it's built for, and where the differences matter.

We built Conduct. We're clearly not neutral. We've tried to be as fair as possible — if anything is wrong, open an issue and we'll fix it.

🎯Orchestration
Conduct AI
conductai.ai
End-to-end AI agent workflows with human approval gates and team spend governance.
🤖AI Assistant
GitHub Copilot
GitHub / Microsoft
Inline code completion and chat inside the editor. Copilot Workspace for multi-file tasks.
🧑‍💻Agent
Devin
Cognition
Fully autonomous coding agent that takes issues end-to-end in a sandboxed environment.
🐇Code Review
CodeRabbit
CodeRabbit
AI-powered PR review comments — line-by-line feedback and summary on every pull request.
📊Analytics
LinearB
LinearB
Engineering metrics platform — cycle time, DORA metrics, and team health dashboards.
⚡AI Assistant
Bito
Bito
AI coding assistant with customisable org-level prompts and a security scanner plugin.
☁️AI Assistant
Amazon Q Developer
Amazon Web Services
AWS-integrated coding assistant with code review, security scanning, and CLI support.
🔒Security
xHawk
xHawk Security
SAST/DAST security scanning integrated into CI/CD with policy gates and compliance reporting.
Feature matrix

Side-by-side comparison

✅ Available 🟡 Partial / limited ❌ Not available Based on public docs · June 2026
Filter:
Feature Conduct AI Copilot Devin CodeRabbit LinearB Bito Amazon Q xHawk
In depth

Tool-by-tool breakdown

Decision guide

Which tool fits your situation?

The structural divide

Every platform governs AI at the output layer.
Guard governs at the tool-call layer.

Most tools reviewed here see what the agent produced. ConductGuard hooks into Claude Code, Cursor, Codex, and Windsurf at the tool-call level — before every file write and bash command executes. That's a layer no other platform in this comparison reaches.

Devin Desktop
Output layer: you see the PR the agent opened. What it tried, abandoned, and rewrote in the sandbox stays invisible. No policy enforcement until after execution ends.
Rayfin / Augment Cosmos
Observation layer: aggregates outcomes and surfaces insights. Tells you what happened. Doesn't intercept what's about to happen — no blocking, no pre-execution spend control.
ConductGuard
Tool-call layer: hooks fire before the file write. Before the bash command. Before the API call. BLOCK, WARN, or AUDIT — with spend attributed and policy checked, before a single token hits your codebase.
"Devin, Rayfin, and Cosmos see what the agent produced. Guard sees what it's about to do.
That's the difference between observing AI and governing it."

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