Corla is the context layer between what your organisation knows and what your AI agents do. Publish once. Every agent — across every team, every role, every vendor — works from the same ground truth. Consistent, scoped, and audited.
Most engineering organisations have deployed AI coding tools. What they haven't done — because there was no infrastructure for it — is made those agents consistent, current, or organisationally aware.
Each team sets up their own agent context in local files, maintained by nobody, going stale immediately. The same standards get copy-pasted across dozens of projects with 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 source. Architecture changes, deprecations, approved libraries — propagated to every agent automatically, on every session start.
Your system prompts, playbooks, and architecture knowledge are intellectual property. Corla ensures that what reaches any developer — internal or external — is a compiled derivative. The source never crosses the boundary. Ever.
Incidents happen. Lessons get encoded into context packages. From the next session, every engineer's AI agent — including new hires and vendors — operates with awareness of that failure mode. It compounds.
Agents that reason together and reach conclusions — grounded in shared enterprise context, scoped by role, and fully auditable. Not task execution. Structured deliberation that produces judgements the enterprise can act on.
The system is designed to disappear after setup. Developers use the tools they already use. 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 organisation knows — the current standards, the approved patterns, the latest deprecations. No manual steps. No stale local files.
Most organisations have a post-incident review process. Very few have a mechanism to turn those lessons into something every AI agent actually acts on. Corla closes that loop.
An organisation using Corla for a year has a context broker that encodes every architecture decision, every deprecated pattern, every hard-won production lesson — live, in every agent's context window, 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 distils it into a context update — a new entry in the "what not to do" package.
The package is versioned and published. Takes minutes. No individual needs to update anything locally.
From the next working session, every engineer's AI agent — including new hires and vendors — operates with awareness of the failure mode. It doesn't happen again.
Corla extends beyond context delivery into a coordination layer for multi-agent workflows — across teams, machines, and vendor boundaries. Every exchange is scoped, logged, and grounded in shared enterprise standards.
A frontend team's agent and a backend team's agent surface contract mismatches before either side ships — without sharing codebases, without a synchronous meeting, without a human relay.
A coordinated review agent checks every PR against current architecture standards, approved libraries, and the latest "what not to do" package — consistently, before a human reviewer opens it.
An on-call agent and an SRE agent work a shared investigation. Both are 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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We onboard in cohorts with dedicated support. First 10 developer seats are free during the pilot period.