I've been working on an AI-agent system where the difficult part isn't remembering previous incidents.

It's deciding **when a previous solution should no longer be trusted**.

I built FULFILL-X / CASCADE to explore this using Hindsight for persistent agent memory.

The basic idea is:

**Experience → Recall → Reflect → Learn → Challenge → Adapt**

Instead of treating a recalled experience as an instruction, the system extracts the conditions under which a strategy worked, its failure boundaries, supporting evidence, contradictions, and confidence.

One example made the problem concrete.

A historical supplier-substitution strategy worked when the supplier had roughly **45% spare capacity**.

In a later incident, capacity dropped to **38%**.

The system doesn't simply retrieve the old successful action and replay it. The current condition crosses the learned capacity boundary (>40%), so the historical strategy becomes partially/non-transferable.

The agent then changes its behavior and selects an alternative recovery strategy using emergency inventory reserves.

That distinction is what I found interesting:

**memory retrieval ≠ permission to reuse a decision**

The system also keeps uncertainty explicit:

* KNOWN
* PROBABLE
* UNCERTAIN
* UNKNOWN

Learned recovery skills can then be challenged under changed conditions before becoming trusted strategies.

I evaluated the behavior over 30 deterministic episodes:

* Recovery success: 43.3% → 93.3%
* Repeated mistake rate: 43.3% → 0%
* Unsafe action rate: 56.7% → 3.3%
* Boundary avoidance: 0% → 46.7%
* Evidence reuse: 0% → 96.7%
* Average prediction error: 2.32h → 0.39h

The part I cared about most was verifying that memory actually changed decisions rather than merely existing somewhere in the architecture.

The project is open source:

[https://github.com/manidharreddy0909/full-fill-x3](https://github.com/manidharreddy0909/full-fill-x3)

I'd be interested in how others would approach this problem:

**What signals should an AI agent use to decide that a previously successful strategy is no longer safe to reuse?**

How should an AI agent know when not to reuse a successful strategy?