If you’re building agentic workers, you’re probably drowning in data and none of it feels quite right to keep. Storing every scrap of operational noise isn’t just expensive and messy, it crams your agent’s mind full of useless clutter. Humans don’t do this. We remember what matters, which is usually what surprises, embarrasses, intrigues, or alarms us. I want to explain why that’s not an accident but a design principle, and how a better agentic memory system one built around exception can jump your organisation from data hoarding to learning.
Agentic workers don’t need sleep. Which means if you let them, they’ll watch and log everything, filling your system with a non-stop torrent of operational exhaust. You get ballooning storage costs, a blizzard of low-value logs, and agents so swamped by noise that key signals actual opportunities to learn are drowned out. So, here’s the hard question: What should an agent remember, and what should it confidently ignore?
For the technical mechanics of keeping only what counts, I recommend reading Unsupervised anomaly detection with memory bank and contrastive learning. As Yuhao Sun and colleagues put it, “To overcome memory inflation and signal-to-noise issues, we propose a memory bank architecture that selectively retains representative ‘anomalous’ events detected via contrastive learning, discarding redundant operational noise.” This idea selectively storing exceptions, not all activity is absolutely foundational.
Think about it from first principles. People don’t recall every commute, lunch, or staff meeting but you remember the time your car broke down on the motorway, or when a demo crashed in front of the board. As I like to say, “Humans compress life by defaulting to ‘normal’ and storing ‘exceptions’.” This is not just efficiency; it is the key to real improvement.
Research in agentic memory systems is converging on the same logic. Wenjie Wu and team, studying exception handling in LLM-driven workflows, highlight: “SHIELDA operationalizes exception triggers in LLM-driven workflows as first-class memory events, enabling agents to structure, retrieve, and reason over exceptions such as 'surprise', 'anomaly', or 'deviation from norm' rather than treating all logging data as equally important.” (Structured Handling of Exceptions in LLM-Driven Agentic Workflows)
If agents are going to learn, they should store only what truly matters. From operational experience and more than a few AI missteps I see four main triggers for useful memory events:
For those building memory systems, see How to Design Efficient Memory Architectures for Agentic AI Systems: “Building agentic memory means structuring data into retrievable memory objects each tagged with its trigger (e.g., surprise, error, risk) and then filtering or decaying objects that lack lasting organisational value.” It’s not about logging all data, but tagging useful exceptions, storing them efficiently, and allowing the unremarkable to decay or vanish over time.
When a trigger fires, your agent creates a memory object but not an amorphous note. It should be structured:
This isn’t abstract see the SHIELDA architecture (link) for a technical pattern on how exceptions become memory objects and how memory is pruned.
A true learning agent does more than spot one-time anomalies. The real gold is in “rediscovery” when repeat incidents form a pattern. As the editorial at ExperioLabs notes, “Continuous organizational learning requires surfacing patterns not just from new discoveries, but also from purposeful rediscovery identifying repeating knowledge gaps or recurrent errors so they can be codified and acted upon.” (Unlocking Organizational Intelligence)
In practice, this means distinguishing between what’s genuinely new (discovery), and what’s proof of a recurring gap, error, or anomaly (rediscovery). Only with robust rediscovery can you stop teams from repeating mistakes that were already solved three quarters ago.
Not all exceptions age equally. Some are red-hot (last week’s supply chain miss), others are slow-burning but crucial (fire exits, regulatory exposures). Retrieval should balance:
A system that weights both lets agents (and their human handlers) bring forward what’s most likely to drive action, not just what’s recent or loud.
All these ideas are nice, but you know my bias philosophy must meet practice. So, what does this look like in testing? The team at Atera documents their approach: “In one of our agentic AI pilots, we instrumented a test rig to inject exceptions surprise, distrust, and error measuring not just outcome accuracy, but how quickly and effectively the system surfaced action-worthy anomalies.” (9 mind-blowing Agentic AI experiments happening right now)
A practical rig exercises agents with real and artificial triggers:
Let’s be direct: risks are real.
Mitigate with thresholds, decaying memory, team feedback, and strong filters (including similarity checks and baseline tolerances).
This four-trigger framework is a start, not an endpoint:
If you take nothing else from this piece, let it be this: more data does not equal more knowledge. The agents we build must be learners, not hoarders. Selective, exception-driven memory creates organisational learning that is visible, actionable, and crucially sustainable.
For further reading and practical frameworks:
Time to stop hoarding and start learning.