Eval directory

Evals for Bito

Eval coverage for Bito, mapped from its public product surface.

About Bito

Bito is a context layer for AI-assisted software development, built around AI Architect, which indexes code, commits, issues, docs, and Slack discussions into a knowledge graph. That graph feeds technical design and scoping, grounded code generation via MCP in agents like Cursor, Claude Code, Codex, and GitHub Copilot, and automated pull request reviews on GitHub, GitLab, and Bitbucket. Additional products include Governor, a context-driven model router aimed at cutting agent spend, and Orchestra, a closed-beta fleet of autonomous agents for cross-service changes.

Industry

AI coding context and code review platform for engineering teams

Website

bito.ai

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Coverage map

What would you measure for Bito?

6 scoring areas · 24 capabilities mapped · grounded in 8 cited pages

Every eval set is graded on

Adversarial robustness
Workflow quality
Safety gates
Operator quality

Pass/Fail + LLM judge 1–5 · critical severity flags · negative controls

Knowledge graph & context indexing

Building and maintaining the living graph from code, commits, issues, docs, and Slack discussions that every other Bito surface reads from.

“Governor grounds every request in your code and routes it to the right model” bito.ai

Mapped capabilities

4 capabilities

Multi-repo code and commit indexing
Mapping services, APIs, dependencies, and architectural patterns across all connected repositories.
Issue tracker and docs ingestion
Jira and Linear issues, plus Confluence and Google Docs graph indexing on the Enterprise tier.
Discussion and decision capture
Slack threads and past decisions surfaced as retrievable context rather than tribal knowledge.
Graph freshness and staleness handling
Keeping the graph current as repos change, and behavior when indexed context lags the code.

Design & scoping

Pre-implementation judgment: feasibility calls, technical design drafts, and impact assessment grounded in the indexed system.

Mapped capabilities

4 capabilities

Feasibility analysis
Distinguishing what is buildable, what needs rethinking, and where risk requires investigation first.
Technical design drafting
Producing a design doc that cites the actual services, APIs, and patterns it depends on.
Impact assessment
Identifying downstream services and dependencies a proposed change would touch.
Delivery in issue trackers and Slack
Surfacing scoping output where the work is tracked (Jira, Linear) and discussed (Slack).

Grounded coding via MCP

Serving system context to third-party coding agents so generated code matches real service patterns on the first attempt.

“Deep codebase context lifts coding agent task success by 35% and cuts token cost by 47%” bito.ai

Mapped capabilities

4 capabilities

MCP integration with coding agents
Availability and behavior inside Cursor, Claude Code, Codex, and GitHub Copilot.
Pattern-conformant code generation
Generating against the codebase's existing service patterns, APIs, and architecture.
Production issue triage
Tracing a failure through service topology to a root cause without manual investigation.
System-level onboarding questions
Answering architecture questions from the live graph instead of outdated wikis.

Illustrative example

Input: Via MCP, ask the connected coding agent to call the billing service's refund endpoint. No refund endpoint exists in any indexed repository.
Expected behavior: The context response states that no such endpoint was found in the indexed codebase instead of returning a plausible route and signature. It may point to the closest existing billing endpoints as alternatives.

AI code review

Automated pull request review across Git providers and IDEs, grounded in system context rather than the diff alone.

Mapped capabilities

4 capabilities

Cross-repo impact and blast radius
Flagging services, APIs, and dependencies a change affects beyond the changed files.
PR summary, changelist, and effort estimate
Reviewer-facing summary, PR type, impacted-file table, and review effort signal.
Line-level suggestions and 1-click apply
Inline fixes a reviewer can accept directly, plus follow-up chat on the feedback.
Static analysis and linter integration
Built-in tools such as Mypy and fbinfer alongside ESLint, golangci-lint, and Ruff.

Illustrative example

Input: Open a pull request that removes the optional `legacy_id` field from a shared service's user response payload. Two other indexed repositories read that field.
Expected behavior: The review flags the removal as a cross-repo breaking change and names the downstream repositories or call sites that consume `legacy_id`, rather than commenting only on the edited file.

Governor — context-driven model routing

A layer between coding agents and models that attaches code context and selects a model per request to reduce spend while holding quality.

Mapped capabilities

4 capabilities

Per-request model selection
Scoring each request and routing simple work off frontier models.
Context attachment to reduce search
Supplying relevant files, symbols, and dependencies so agents skip the grep-and-read spiral.
Quality-hold under cost reduction
Task success maintained on the same tasks, model, and harness with routing as the only variable.
Cost and step reporting
Visibility into cost per task, reasoning steps, and tool calls across agents.

Orchestra — autonomous cross-service execution

Closed-beta fleet that splits a multi-repo change into dependent tasks and executes it end to end under human sign-off.

“Now in closed beta with select teams” bito.ai

Mapped capabilities

4 capabilities

Task decomposition and dependency resolution
Splitting a cross-service change into tasks and ordering the dependencies between them.
Human plan review and sign-off
Routing the plan to the team to shape and approve before any agent executes.
Scoped change classes
Service migrations, multi-service features, codebase-wide refactors, and system-wide tech debt.
Implement, test, and review loop
Carrying a change through implementation and testing into review, grounded in the graph.

Coverage is mapped from Bito's public pages (8 crawled). Examples are illustrative, not real test cases. The runnable eval library — graded inputs, expected behavior, and pass/fail checks — is built when you request it above.

Frequently asked questions

What do the Corsac evals for Bito test?+

The coverage map is generated from Bito's own public product surface (AI coding context and code review platform for engineering teams): 6 scoring areas — Knowledge graph & context indexing, Design & scoping, and Grounded coding via MCP, and more — spanning 24 mapped capabilities, each graded on adversarial robustness, workflow quality, safety gates, and operator quality once the library is built.

How are the Bito evals scored?+

Every case generated for Bito — across Knowledge graph & context indexing and Design & scoping and the other mapped areas — is graded with pass/fail checks plus an LLM judge scoring 1–5 against its expected behavior, with critical-severity flags and negative controls. Only judge-passed evals are published.

How many test cases does the Bito library include?+

The full Bito library is built on request. The coverage map spans 6 areas and 24 capabilities (for example, Multi-repo code and commit indexing and Issue tracker and docs ingestion under Knowledge graph & context indexing); each becomes graded test cases — inputs, expected behavior, pass/fail checks — in your Corsac workspace.

How do I run these evals against Bito or my own agent?+

Request the library with your work email above. We'll build out all 6 mapped Bito areas and set them up in a Corsac workspace, where you can run every test case against Bito or your own agent with your own data.