Execution control plane for AI systems

Control what AI actually does.

feralhogs sits between your models and your systems, turning probabilistic intent into governed, safe execution.

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model.intent write_customer_refund()

policy.check requires read-before-write context

context.verify missing account state

decision block + repair plan

trace captured for replay

Core thesis

LLMs do not fail at capability. They fail at execution.

Problem

Production failure is a control problem.

  • AI systems generate tool calls and actions.
  • Those actions often execute with minimal validation.
  • Incorrect behavior becomes real side effects.
  • Failures are silent, difficult to reproduce, and hard to trust.

Solution

The model proposes actions. feralhogs decides what actually runs.

  • Validate tool calls before execution.
  • Enforce policy and operational constraints.
  • Block or repair invalid actions.
  • Manage execution order and preserve traceability.

How it works

Put an execution authority between AI intent and real systems.

  1. 01Client sends a request to an OpenAI/Anthropic-compatible endpoint.
  2. 02feralhogs forwards the request to the model.
  3. 03The model proposes tool usage.
  4. 04feralhogs intercepts, validates schema, checks policy, and verifies context.
  5. 05Approved tools execute under feralhogs control.
  6. 06Results flow back into the model loop.
  7. 07The client receives final output plus a full trace.

Positioning

Not another framework. Not orchestration glue. Not logging.

Not

  • Another agent framework
  • Orchestration glue
  • Logging dressed up as control

Instead

  • Execution runtime
  • Policy enforcement layer
  • Decision control plane

Frameworks

Help AI do more.

Useful for composing systems. Not sufficient for governing live side effects.

Agents

Execute work.

Powerful, but they still need an authority layer deciding what is allowed.

feralhogs

Governs what work can happen.

Only valid, policy-compliant operations reach your systems.

Why now

Capability is outpacing control.

AI is moving from demo to production. Tool ecosystems are expanding the surface area. Enterprise teams keep rebuilding the same execution safeguards internally.

Production adoption stalls when trust is low.

Enterprises need control, auditability, and predictable execution before they expand deployment.

Tooling surface area keeps growing.

MCP and tool ecosystems make models more useful, but also widen the blast radius of bad execution.

Teams are rebuilding this layer repeatedly.

feralhogs turns an internal one-off pattern into a productized execution runtime and control plane.

Target users

Built for teams that cannot let models touch production systems unchecked.

Teams integrating AI into existing products
PE portfolio companies modernizing systems
Enterprise engineering orgs with real side effects
Anyone uncomfortable with direct model-to-system access

Product shape

Runtime below. Control plane above.

ferald

Runtime

  • OpenAI/Anthropic-compatible endpoint
  • Owns tool dispatch
  • Enforces policy at execution time

Control plane

Operational visibility

  • Trace capture
  • Replay
  • Policy configuration
  • Audit trail

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Don’t let your AI run wild.

From AI intent to controlled execution. If you are putting models in front of systems that matter, feralhogs is the control layer.

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