Blog
How AI agents spend tokens on large, multi-repo codebases — and what changes when they query one shared index instead of rediscovering structure every session.
-
"Done" Doesn't Mean Built as Described. Here Is How Oracus Checks Every Ticket Against the Code That Shipped
A ticket describes what should happen. A PR merges, someone flips the ticket to Done, and nobody checks that the code actually did what the ticket asked. Oracus reads each acceptance criterion the moment a ticket closes, verifies it against the code, specs, tests, and config that shipped, and comments back with evidence, flagging anything it can't confirm.
Read more → -
Your Docs Are Always Out of Date. Here Is Why, and How Oracus Keeps Them Current Automatically
Product docs drift the moment a feature ships, because keeping them current is manual work spread across developers, POs, and writers. Oracus reads the code and the tickets every night, drafts grounded and cited documentation, and keeps it fresh. Or it serves the same knowledge live over MCP.
Read more → -
Why AI Coding Agents Burn Tokens on Large Codebases, and How a Shared Index Cuts the Cost
On a large, multi-repository codebase, an AI coding agent spends most of its token budget rediscovering structure it has no memory of. Here is the mechanism behind that cost, and why querying a pre-built index changes the math.
Read more →