Bito

We ran a normal ten turn session through a coding agent. Trace a Slack event through Kafka. Find where OAuth lives. Check whether a Jira integration already exists. The session cost $6.81. $5.30 of that went to the agent grepping and reading files before it could do anything with them. The compounding explains it. Each step sends everything gathered so far back to the model, so a file read on turn two still rides along on turn twenty. Every token the agent gathered went back through the model about 50 times. We reran the same ten prompts with Bito's AI Architect connected. AI Architect keeps a live index of every repo, resolves each question to the relevant code, and hands the agent that span before it starts hunting. Same model, same caching. With nothing left to search for, the agent read 3 files instead of 62 and pushed 1.4 million tokens through the model instead of 5.3 million. $1.13. 8.5 minutes instead of 25. Five runs per arm, and it held every time. AI Architect reaches your agents through Governor, the layer that sits between your coding agents and your models. One environment variable, and nothing else changes. Full breakdown here: https://lnkd.in/gt8kwQrk

Model prices keep falling, and agent bills keep climbing anyway. Watch a session run and you see why. The agent greps the codebase, re-reads its own transcript on every step, and spends most of its budget locating where the change belongs rather than making it. Bito's Governor removes that search. It sits between your coding agents and your models, attaches a map of the relevant files and dependencies to each request, then routes that request to a model sized for the work involved. On a customer A/B, same tasks and same harness, cost per task fell from $4.12 to $2.14 while success held at 100%. Link in comments.

Everything your team does in the IDE now runs from a Slack thread. → Sprint standup brief on demand → Feasibility and impact on a PRD → Grounded implementation plan → Merge request from a thread → Production issue triage Bito's AI Architect carries the same system context in Slack that grounds coding agents in Cursor, Claude Code, Codex, or any MCP client. 10 ways teams are using it, below:

Autonomous agents are now in closed beta 🎉 Give them a spec. A fleet works every repo the change touches, all the way to a reviewed pull request. Migrations, cross-service features, refactors, tech debt. Your engineers review at every stage. More here: https://lnkd.in/dNeSwpxQ

Bito's AI Architect now reads your Google Docs. Engineering teams keep PRDs, design decisions, and technical specs spread across Google Docs. That context has been invisible to coding agents and code reviews until now. AI Architect now pulls from Google Docs the same way it already pulls from Confluence, Jira, Linear, Slack, and your codebase. One more surface feeding the same knowledge graph. Connect your Google account at alpha.bito.ai. 

Bito's Slack agent leveled up this month! Code reviews now run inside Slack, you can open PRs and MRs straight from a thread, and AI Architect learns from the feedback your team gives in Slack and Jira. Learn more here:

Conversational learning in Slack and Jira is live in AI Architect. The decisions that shape your architecture live in Slack and Jira. Why a service got split? Why a migration stalled? Why the workaround still exists? Your team tags Bito with that reasoning, and it enters the knowledge graph as a durable rule that grounds every AI interaction across the team. See how it works: https://lnkd.in/gXMm_2JY

10 reasons engineering teams use Bito's AI Architect. From technical design in Jira and Linear to grounded code generation in Cursor and Claude Code to codebase aware code reviews on every pull request. Swipe through for the highlights. Read the full post here: https://lnkd.in/gAYBaXzb