Compare/Intent vs Cody Enterprise 3.0

AI tool comparison

Intent vs Cody Enterprise 3.0

Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.

I

Developer Tools

Intent

Describe a feature. Agents build, verify, and ship it — in parallel.

Ship

75%

Panel ship

Community

Free

Entry

Intent, from Augment Code, reimagines the coding agent as an orchestrated team rather than a single assistant. You write a feature spec in plain language. A Coordinator Agent breaks it into tasks. Specialist Agents execute those tasks in parallel inside isolated git worktrees. A Verifier Agent checks results against your original spec before surfacing anything for your review. The spec is "living" — it updates as work progresses, and when requirements change, updates propagate to all active agents. This is meaningfully different from one-shot prompting or even multi-step agentic coding. Intent is designed for enterprise teams working on large codebases where a single feature might touch dozens of files across multiple services. The built-in Chrome browser lets agents preview local changes without leaving the workspace. It integrates with existing git workflows rather than replacing them. Launched in public beta February 2026 (macOS only, Windows on waitlist), Intent got its highest visibility yet when it hit Product Hunt with 302 votes this week. Augment Code has been quietly building toward this: their previous focus on large-enterprise codebase indexing gives Intent's retrieval layer an advantage over agents starting from scratch.

C

Developer Tools

Cody Enterprise 3.0

AI coding assistant with unlimited multi-repo context and SOC 2 audit logs

Ship

100%

Panel ship

Community

Free

Entry

Cody Enterprise 3.0 is Sourcegraph's AI coding assistant built for large engineering organizations, extending context retrieval across unlimited repositories simultaneously so developers get answers that understand the full codebase. It adds SOC 2-compliant audit logging for every AI interaction, satisfying the compliance requirements that block enterprise AI adoption. Bring-your-own-model support lets teams swap in their preferred LLM without losing the context layer.

Decision
Intent
Cody Enterprise 3.0
Panel verdict
Ship · 6 ship / 2 skip
Ship · 4 ship / 0 skip
Community
No community votes yet
No community votes yet
Pricing
Public Beta — Free during beta (macOS only)
Enterprise pricing (contact sales); Cody Free tier available for individuals
Best for
Describe a feature. Agents build, verify, and ship it — in parallel.
AI coding assistant with unlimited multi-repo context and SOC 2 audit logs
Category
Developer Tools
Developer Tools

Reviewer scorecard

Builder
80/100 · ship

The living specs concept is the right idea — autonomous coding agents fail because requirements get lost mid-task. Keeping a maintained spec that agents reference throughout solves the context drift problem. Isolated workspaces mean you can run parallel feature development without race conditions. This is a serious tool for serious teams, not a toy.

78/100 · ship

The primitive here is honest and specific: a context retrieval layer that indexes across unlimited repos and pipes relevant code into whatever LLM you bring. That's a real problem — the moment your codebase spans more than one repo, GitHub Copilot and Cursor both go partially blind. The BYOM configuration is the right DX bet; it puts complexity in config where it belongs and lets the context engine be the actual product rather than a forced model subscription. The moment of truth is asking a question that spans three repos — if that actually works without hallucinating package boundaries, this earns its enterprise price tag. What I want to see is the indexing pipeline documented: how fresh is the context, what's the staleness model, and does it handle monorepos differently than polyrepos? Those aren't marketing questions, they're the whole product.

Skeptic
45/100 · skip

Every multi-agent coding tool in 2026 promises to 'build, verify, and ship' features autonomously. Most of them generate plausible-looking code that compiles but doesn't actually work as intended. Augment Code has solid underlying models but 'coordinated agent teams' still means you're debugging AI-generated code at the seams between agents. Until I see real production deployments with zero-intervention feature shipping, this is glorified autocomplete with extra steps.

72/100 · ship

The direct competitors are GitHub Copilot Enterprise and Cursor with codebase indexing — and neither of them has Sourcegraph's decade of code search infrastructure underneath. That history is the actual moat, not the AI wrapper on top. Where this breaks: organizations with highly fragmented access controls across repos, where the context retrieval either over-fetches (security problem) or gets permission-gated into uselessness. The SOC 2 audit logs are table stakes for any enterprise deal in 2026, so calling that a feature is a bit rich — but shipping it before competitors formalized it matters. What kills this in 12 months: GitHub ships deeper Copilot Enterprise context natively and the org that was already paying for GitHub Enterprise doesn't want a second line item. Sourcegraph survives that only if the context quality gap stays wide enough to justify the cost.

Futurist
80/100 · ship

Intent represents the transition from AI-assisted coding to AI-directed development. The living spec paradigm is a genuine architectural insight — specs as shared context between agents and humans is how autonomous software teams will be organized. Augment's bet on coordination over raw capability is the right design philosophy as models plateau in coding benchmarks.

No panel take
Creator
80/100 · ship

The spec panel that tracks requirements in real time is a design win — it makes AI development legible to product managers and designers, not just engineers. Seeing what agents are doing across isolated workspaces without reading logs is the kind of transparency that actually builds trust in AI tooling.

No panel take
Founder
No panel take
75/100 · ship

The buyer is the VP of Engineering or CISO at a company with 200+ engineers across multiple repos — this is a clear, checkbook-holding persona, and SOC 2 audit logs are specifically the procurement unlock that moves deals out of legal limbo. That's a real wedge. The BYOM configuration is smart revenue-defensibility: Sourcegraph becomes the context layer that persists regardless of which model wins the next benchmark cycle, insulating them from the commodity model price war. The risk is the expand story — once they land an enterprise, what does deeper adoption look like? If it's just more seats, they're a seat-count business, and seat-count businesses get squeezed when headcount freezes. The specific decision that makes this viable is owning the index, not the model — the index is sticky, the model is not.

PM
No panel take
71/100 · ship

The job-to-be-done is clean: get an accurate, context-aware answer about code that lives in more than one repository without switching tools or copy-pasting context manually. That's one job, no 'and.' Onboarding for enterprise is always an IT/procurement journey, not a 2-minute trial, so I won't penalize that — but the individual free tier needs to get a solo dev to a cross-repo answer in under 5 minutes or it never seeds the enterprise deals. The product opinion is strong: Sourcegraph has committed to the context layer being the product, which means they're not trying to win on model quality. That's the right call given their history. The gap is that 'unlimited repositories' as a marketing claim needs to be stress-tested publicly — if there's a practical ceiling at 50 repos or 10M LOC, that needs to be in the docs, not discovered during a pilot.

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