Why Ctxo?

AI agents don't fail because they can't code. They fail because they code blind.

Proactive, not reactive

The core shift: your agent stops reacting to files it stumbles into and starts planning from a complete map. Blast radius before the edit. Git intent before the bug fix. Importer list before the rename.

The agent still writes the code. It just stops writing it blind — so the bug never has to be caught by the compiler, the tests, CI, or a user.

Modern AI agents are fluent at writing code. They are not fluent at understanding what it touches. Every unseen dependency, every reverted pattern, every subclass that nobody warned them about becomes a bug that surfaces later - where fixing it is exponentially more expensive:

Stage where a blind edit is caught	Cost to fix
Compile / type-check	Minutes. One more iteration.
Unit tests	Tens of minutes. Re-run, re-diagnose, re-edit.
Integration / CI	Hours. Someone else is blocked.
Runtime / production	Days. Users hit it. A revert is on the table.

Each stage is reactive. The agent writes, something breaks, the agent patches, something else breaks. Token budgets balloon, latency compounds, dev time drags, trust in the agent erodes.

Ctxo flips this. Before the agent writes a single line, get_blast_radius tells it every caller and subclass that will be affected; get_why_context surfaces the revert from three weeks ago; get_logic_slice delivers exactly the deps it needs inside the token budget. The problem never happens - it is resolved at authoring time, not caught downstream.

The problem: agents code blind

Drop a modern coding agent into a real repo and watch it work. It will:

1. Ripgrep for a symbol name - and get 47 hits across tests, docs, and generated code.
2. Read five files to figure out which hit is the definition.
3. Read five more to find the callers.
4. Miss the class that extends it entirely, because inheritance doesn't show up in a text search.
5. Never check git history - so it confidently reintroduces a bug that was reverted three weeks ago.
6. Run out of context halfway through the task and start hallucinating.

This isn't a skill gap. It's a sensory gap. The agent has no map. It navigates your codebase the way you would navigate a city with your eyes closed and a phone book.

The solution: a pre-built index and semantic tools

Ctxo indexes your repo once (and keeps it fresh via file watchers and git hooks) into a deterministic graph:

• Every symbol (function, class, method, interface, type)
• Every edge (imports, calls, extends, implements, uses)
• Every relevant git commit that touched each file, with intent classified
• Every anti-pattern (reverts, repeated hotfixes, thrash)

Then it exposes that graph through 14 semantic MCP tools. One get_blast_radius call replaces a whole ripgrep/read spiral. One get_pr_impact replaces an entire PR review session of "wait, what calls this?"

Without Ctxo vs with Ctxo

Measured on a typical "rename a widely-used function" task across a mid-sized TypeScript repo:

Metric	Without Ctxo	With Ctxo
Tool calls to map impact	10-20 (grep + read)	1 (get_blast_radius)
Tokens burned on orientation	40k-80k	2k-4k
Context window free for actual work	~20%	~85%
Reverted-bug reintroduction risk	High (no history)	Low (get_why_context)
Missed downstream dependent	Common	Rare (full transitive graph)
Time to first useful edit	Minutes	Seconds

See it in action

Two interactive comparisons show Ctxo against a naive agent loop:

• Blast Radius Comparison
  • grep-and-pray versus one get_blast_radius call
• Dead Code Comparison
  • manual tracing versus find_dead_code

And the Ctxo Visualizer lets you explore the dependency graph of a real indexed repo.

Next steps

• Installation
• Quick Start
• MCP Tools Overview - the 14 tools in detail