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CONTROL
Versioned AI agents and platform-enforced execution policies that make every run reproducible, auditable, and bounded.
WHY AI SYSTEMS DRIFT
Ask most teams where their AI system's behavior is defined and the honest answer is a prompt, a service, and some code between them. Change any one and the other two drift. Run the same input twice and the explanation for the difference is a shrug.
Definition scattered across prompts, services, and code, with no version of the whole thing. Nobody can say exactly what is running.
Access to a tool is access to everything behind it. A prompt instruction is not enforcement, and a prompt injection overrides it.
When something goes wrong, there is no log of which policy was in effect, what data was accessible, or what tool calls were made. The audit question has no answer.
HOW CONtrol WORKS
Most AI systems distribute behavior across prompts, services, and configuration files that no one fully owns. Meibel puts the definition in one place, freezes it when you publish, and enforces the boundaries before the model runs.
An agent is a draft until you publish it. Publishing captures the full configuration at that moment and freezes it. Published versions are immutable, and the same version number always produces the same behavior given the same inputs and datasource state. If a new version performs worse, revert to the previous one. Testing becomes meaningful because there is a fixed thing to compare against.
Constraints are enforced by the platform outside the model's execution path, so non-compliance is structurally impossible rather than discouraged. An execution policy narrows what a session can access across data and tools. Enforcement happens before the model runs: the schema it sees is modified, queries are rewritten at the platform level, and results are filtered before they arrive. The model never sees what it is not allowed to see.
A batch job runs the same configuration that was tested interactively. One document or a million, the definition does not change. There is no rewrite for production, no separate pipeline for bulk. The configuration that works is the configuration that runs.
BUILT FOR PRODUCTION AT SCALE
Meibel Control is designed for teams that cannot afford emergent behavior in production.
A platform serving thousands of organizations runs one agent. Each session carries a policy scoped to that organization. One customer's data cannot appear in another customer's session, and there is one agent to maintain rather than thousands.
The same agent runs across business units with different data boundaries per unit. R&D material stays outside a marketing query without a second agent existing.
A services firm builds a solution once and deploys it per client, isolated by the platform rather than by code the firm has to write, test and defend.
A team ships an improvement. The previous version stays live in production. If the new one underperforms, the rollback is a version number, not a reconstruction from memory and logs.
The organization changed the underlying model. The execution policy did not change. Guardrails, routing rules, and quality thresholds carried forward because they live outside the model.
The agent was tested on a single document. The same configuration runs on ten thousand. Batch and interactive execution share one definition. Nothing was rewritten for production.
WHAT ENTERPRISE AI TEAMS BUILD WITH MEIBEL
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Define it once. Publish it. Let the platform hold it.
Execution policies are not versioned. Only agent definitions are versioned. A policy is a constraint applied at runtime. If a policy changes, the new constraint takes effect for future sessions.
Prompt injections cannot override execution policies. Policies are enforced at the platform level, outside the model's execution path. The model does not receive the data or tool access that a policy excludes, so there is nothing to override.
Yes. The same published version runs in both modes. Batch jobs use the same definition as interactive runs. Nothing is rewritten for scale.
No. Execution policies scope what an agent session can access within Meibel. They do not replace your identity and access management layer. They work alongside it.
Every session produces an audit record that includes the policy in effect, the datasources that were reachable, the queries that ran, and the results that were returned. The record is available immediately after execution.
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