Guidance needed: How to design a unified evaluation system for a shared memory layer across multiple agents?

[ZLEternity]

Hi everyone,
We are planning to use Hindsight as a shared memory layer across multiple business agents. Our main challenge is how to design a unified, reusable, and sustainable evaluation system to validate real impact after integration.

Core Question

We want to answer this clearly:

After integrating Hindsight, did business outcomes actually improve, where did the improvement come from, and is it sustainable?
In real production workflows, many tasks do not have complete ground truth, so traditional answer-accuracy evaluation is often not enough.

What we’d like guidance on

1. For a multi-agent shared memory layer, what evaluation framework is recommended?
2. When full ground truth is unavailable, how should we build a reliable evaluation methodology?
3. How can we consistently evaluate impact across different agent scenarios, while separating:
   • memory issues (forgetting, stale information, context inconsistency)
   • execution issues (tool usage / calculation logic errors)
   • normal requirement changes (business iteration)
4. Beyond benchmark datasets (LoCoMo, LongMemEval, etc.), are there recommended production-focused evaluation practices or roadmap guidance?

Any practical recommendations on metric design, evaluation workflow, and common pitfalls would be greatly appreciated.
Thanks!

[benfrank241]

Hi @ZLEternity, great question. This is one of the harder parts of running a shared memory layer in production.

The short version of how we'd approach it:

• Two eval layers. Use LoCoMo/LongMemEval-style scoring offline on your own retain→recall traffic (does the right memory come back?), then measure real impact online with a memory-on vs memory-off A/B on the metric the agent exists to move (resolution rate, task success, etc.). Benchmarks tell you the engine works; the A/B tells you it mattered.
• No ground truth? Lean on LLM-as-judge against rubric criteria (was the answer grounded in the retrieved memory?), pairwise memory-on/off preference, and proxy signals you already have (re-asks, corrections, escalations).
• Attribution (memory vs. execution vs. requirement change): log what was retrieved and whether it was used. Hindsight makes this easy: recall returns what it surfaced plus a trace, and reflect returns based_on (the evidence it actually used). Right memory used but still wrong means execution; relevant memory missed or stale means memory; correct answer changed means requirement drift.

For a multi-agent shared layer specifically, a lot of this gets specific to your workflows, and we work with teams on exactly this. If it'd help, reach out via https://vectorize.io/contact and we can set up a working session against your actual agents and data.