Corla is the context layer between what your organisation knows and what your AI agents do. One MCP endpoint. Every team, every role, every vendor — engineers, designers, support, sales, marketing, product — working from the same ground truth in Claude Code, Cursor, VS Code, Windsurf, Claude.ai, and ChatGPT. Consistent, scoped, and audited.
Most organisations have deployed AI tools — coding assistants for engineers, ChatGPT and Claude for everyone else. What they haven't done — because there was no infrastructure for it — is made those agents consistent, current, or organisationally aware.
Each team manages agent context in local files that quickly go stale. Standards get copied across projects, but there is no single source of truth.
Agents have no persistent organisational memory. A developer spends a session building context for their AI tool. Tomorrow they start from scratch. The organisation's hard-won knowledge doesn't accumulate.
External developers need your context to do quality work. Sharing raw documentation exposes IP. Withholding it produces misaligned output. There has been no governed middle ground.
Platform Engineering publishes standards once. Every team's AI agents pull from the same governed source. Architecture changes, deprecations, and approved libraries propagate to every agent at the start of each session.
Your system prompts, playbooks, and architecture knowledge are intellectual property. Corla ensures that every developer, internal or external, receives compiled context instead of raw source material. The source never crosses the boundary.
Incidents happen. Lessons are encoded into reusable context packages. From the next session, every AI agent, including those used by new hires and vendors, works with awareness of that failure mode. Knowledge compounds.
Agents reason together and reach conclusions grounded in shared enterprise context, scoped by role, and fully auditable. Not task execution, but structured AI deliberation that produces judgments the enterprise can act on.
Corla is designed to disappear after setup. Developers keep using the tools they already know, while governed enterprise context flows in automatically.
Standards, architecture context, approved libraries, and "what not to do" packages are authored once and published to the broker. Versioned. Role-scoped. Instantly available to every agent across the org.
corla initOne command configures the project. The broker adapter writes to the IDE config. OAuth authenticates the developer. Role and project scope are established. Done once per project, then invisible.
The developer opens their IDE. Their AI agent already knows what the organization knows, the current standards, the approved patterns, the latest deprecations. No manual steps. No stale local files.
Most organizations have a post-incident review process. Very few have a mechanism to turn those lessons into AI context that every agent actually acts on. Corla closes that loop.
An organization using Corla for a year has an AI context broker that encodes every architecture decision, every deprecated pattern, and every hard-won production lesson. That knowledge lives in every agent’s context window and appears automatically for every new engineer from their first session.
See the full picture →A failure mode surfaces. The team runs a retrospective and writes the PIR.
Platform Engineering distills it into a context update, creating a new entry in the “what not to do” package.
The package is versioned and published in minutes. No individual needs to update anything locally.
From the next working session, every engineer’s AI agent, including those used by new hires and vendors, operates with awareness of the failure mode. The same mistake is less likely to happen again.
A coordination layer for multi-agent workflows across teams, machines, and vendor boundaries is on Corla’s roadmap. Today, the broker delivers context to single agents. Multi-agent coordination will extend that with broker-mediated exchanges, scoped per agent and fully audited.
A frontend team’s agent and a backend team’s agent can surface contract mismatches before either side ships, without sharing codebases, synchronous meetings, or human relay.
A coordinated review agent will check every PR against current architecture standards, approved libraries, and the latest “what not to do” package before a human reviewer opens it.
An on-call agent and an SRE agent work a shared investigation. Both grounded in the same enterprise context. Findings accumulate. Root cause surfaces faster, without a human relay between machines.
Multiple vendor teams on the same engagement align on interfaces through the broker. Neither team sees the other's codebase. The enterprise controls what each party can see. Every exchange is audited.
Corla isn't just about what agents receive, it's about how the humans behind them interact with a shared layer of institutional knowledge. Different roles publish, review, consume, and coordinate through the same broker. The context that reaches each person's agent is scoped precisely to their role and project.
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