Oconee Runtime logoOconee RuntimeEnterprise AI Governance

Engineering

Enable AI-assisted engineering without giving agents unlimited authority.

Use AI aggressively without giving every agent unrestricted authority. Oconee Runtime applies policy where the risk actually is — the repository, the command, the dependency — so most AI-assisted work is untouched and only the actions that warrant it are warned on or refused. The goal is to say yes to capable tools, not to slow them down.

No sales call required.

The control that gets bypassed protects nothing

Engineering leaders are usually offered two options: allow AI tooling and accept the exposure, or restrict it and watch people use personal accounts. The second is worse, because the activity continues without any record of it.

A control only survives contact with a delivery team if it is quiet almost all of the time. That requires being specific about which actions matter, rather than treating every AI interaction as a risk event.

Example scenario

One action, evaluated

Proposed action

Coding agent

Dependency install in a service repository

Context

Actor
Developer
Repository
Internal service — standard sensitivity
Action
Dependency install
Posture
Warn for this signal at this sensitivity

Policy decision

WARN

The engineer sees the warning, keeps working, and the decision is recorded. Blocking would be the wrong response here; the same action against a critical repository is a different question.

Recorded as evidence

  • actor
  • agent
  • action
  • resource
  • context
  • policy
  • decision
  • timestamp
Examples illustrate Oconee Runtime workflows and policy scenarios.

Keeping friction proportional

Enforcement posture is set per signal, so credential exposure can block while a routine dependency install only warns. Teams generally start in a monitoring posture, look at what their engineers actually do, and tighten from evidence rather than assumption.

  • Per-signal posture — monitor, warn or block, chosen signal by signal
  • Repository sensitivity, so strictness follows the resource
  • Exception requests when a block is wrong for a specific case
  • Decisions recorded whether they allowed or refused
  1. Actor
  2. Agent
  3. Action
  4. Resource
  5. Context
  6. Policy
  7. Decision
  8. Evidence

Relevant capabilities

  • VS Code and Cursor extension
  • Claude Code hook
  • Supported browser AI tools
  • Per-signal enforcement posture
  • Exception requests, time-limited or single-use

Start by watching

Monitoring is available on every plan; warn and block depend on tier. That ordering is deliberate — a policy written before you have seen your own data is a guess, and a wrong block is the fastest way to lose a team's cooperation.

Common questions

Will this slow developers down?
It depends entirely on posture, which is set per signal. Most teams start in monitoring, where nothing is refused, and introduce warnings and blocks only for the specific actions and repositories that justify them.
What happens when a block is wrong?
A blocked action can be appealed through an exception request, which can be granted as single-use or time-limited rather than as a permanent hole in the policy.

Related

Other use cases

AI coding agent governance

Agents now run commands, change files and install dependencies, not just suggest them.

Protect critical repositories

The same command is routine in a sandbox and unacceptable in production.

For security teams

Dashboards report AI activity after the fact and change nothing about it.

AI proposes. Policy decides.

All use cases