Author(s): abi
Originally published on Towards AI.
A practical guide to making recovery a learnable decision layer with code, architecture, and production trade-offs.
Vector search works great for “one question, one answer” workflow. But agentic ai system Rework a plan multiple times – and a small mistake in a hurry becomes a compounding error that derails the entire task.
The article discusses the need for an adaptive recovery system for agentic AI, highlights problems with static recovery in dynamic workflows and describes the design of an adaptive recovery router that improves decision making through feedback loops, thereby addressing compounding errors and enhancing performance in recovery tasks.
Read the entire blog for free on Medium.
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Comment: The content represents the views of the contributing authors and not those of AI.
