Beyond Prompting: Self-Repairing Agent Networks

Download our latest white paper on how hierarchical agent networks can repair themselves under cost, risk, and execution constraints, without retraining the underlying models.

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Why Current Agent Systems Break

Many AI agent systems fail not because the model is weak, but because the overall system is poorly structured.

A multi-step agent workflow can break when tasks are decomposed incorrectly, when the wrong model is assigned to a step, when a required tool is missing, or when parameters are not grounded well enough for execution.

Most systems respond with retries, prompt changes, or manual debugging. That approach does not scale.To build reliable enterprise AI, agent systems must be able to improve from execution itself.

image of a traffic control center (for a mobility and transportation)

A New Way to Think About Agentic AI

This white paper introduces Constraint-Aware Self-Repair (CASR), a framework for optimizing hierarchical agent networks through iterative execution-guided repair.
Instead of treating the model as the only thing to optimize, CASR treats the agent network itself as the object of improvement.
That means the system can revise:
  • task decomposition
  • tool allocation
  • model assignment
  • mixed combinations of all three
The result is a more robust approach to agentic AI: systems that do not just execute tasks, but learn how to restructure themselves after failure.
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