Share graph · provenance running forward
Mario Attard
Senior Analyst · Telco delivery · firm
What they dropped, which clusters it landed in, what got elected, and which experiments and findings descended from it. Everyone’s is visible to everyone, symmetrically. Show outcomes, not counts — no totals, no rankings, no rollups.
Dropped
signals with this person attached
Landed in
distinct clusters
Elected
of those, now fields
Descended
experiments and published items
Drops
What descended
Use a structured episodic store with summarised recall, not a raw vector memory
Route through a gateway you control; do not standardise on a vendor's garden
Not yet: realtime voice for AU contact centres above tier-1 triage
Distil to a small model only after the frontier baseline is measured on the same eval
Put backpressure on agent fan-out before you put it on the model
Retrieval or fine-tuning for this?
Which memory layer should a new agent use?
When do we need to move to post-quantum crypto?
Learning without weights: where continual learning actually lands
Memory bake-off
Right model, right task
How fast is fast enough
Small model, back of the store
Let the agent break it
When agents flood the queue
Learning without retraining
The agent that watches the shop
Follow this person’s finds
Following someone whose drops are consistently good is the internal version of the external voice watchlist, and often a better source than any detector.