Learning architecture

How Seam
gets better
every session.

Seam doesn't retrain your model. It learns at the operational layer — picking the right policy, the right context, and the right action sequence for the situation in front of it.

The result is a control plane that improves visibly between deployments, with every change traceable to the data that drove it.

The four layers

Learning, in four moving parts.

Seam separates learning into four cooperating layers so each one can be reasoned about, audited, and turned off independently.

L1 · Signals
Session-level signals
Outcome, latency, deflection, escalation, operator override. Captured per session, not per token.
Signals are derived from the operational outcome of an agent session — not from internal model probabilities. That keeps learning grounded in things operators actually care about and lets domain teams change what "good" means without retraining.
L2 · Policy
Contextual-bandit policy layer
Chooses tools, prompts, and routes per session, given the context features Seam has at hand.
A contextual-bandit-style policy adapts to feedback within hours, not weeks. It can be overridden, audited, or rolled back per agent class — giving you tunability without touching the underlying model.
L3 · Memory
Distilled session memory
Successful patterns are condensed into reusable context fragments. Failures inform what not to surface.
Memory is structured, queryable, and tenant-scoped. Nothing leaks between customers. Operators can inspect what's been learned, prune what's wrong, and pin patterns that should always apply.
L4 · Evaluation
Continuous evaluation harness
Replay any session against any policy version. Diff outcomes before promoting a change.
Because every session is replayable, every policy update gets tested against real history before it ships. No silent regressions. No "looked fine in staging".
Signal taxonomy

What we actually learn from.

SIG · OUTCOME
Resolution
Did the session reach an outcome the operator (or downstream system) accepted? The single most important signal.
Weight: high
SIG · OPERATOR
Override events
When a human steps in to correct, approve, or undo an agent action. Treated as ground truth for that decision class.
Weight: high
SIG · ROUTE
Escalation & handoff
Whether the session escalated to a human, a different agent, or fallback workflow — and at which step.
Weight: medium
SIG · LATENCY
Time to outcome
End-to-end and per-step latency. Penalises policies that get the right answer too slowly to be useful.
Weight: medium
SIG · COST
Tool-call & token budget
Compute and external-API cost per session, weighted against outcome quality.
Weight: medium
SIG · POLICY
Policy violations
Any guardrail trip — data-handling, scope, identity. Always negative; tracked separately from outcome.
Weight: hard constraint
Boundaries

What Seam won't learn.

Operational learning is powerful and easy to overreach. Here's where we draw the line on purpose.

Seam does
Adapt policy from operator feedback
When operators correct an agent, the next session in that class sees the correction reflected in the policy. The change is logged with provenance.
Seam doesn't
Retrain customer models
We don't fine-tune your LLM, we don't pool data across tenants, and we don't ship customer interactions to model providers as training data.
Seam does
Distil session-scoped patterns
Successful patterns become reusable context fragments — within the boundaries of the tenant and agent class that produced them.
Seam doesn't
Learn across customer tenants
Memory, signals, and policy adaptation are strictly tenant-scoped. Cross-tenant transfer requires explicit opt-in and contractual review.
Seam does
Replay policy updates against history
Every policy change is evaluated against past sessions before it's promoted. Regressions surface before users feel them.
Seam doesn't
Auto-promote without human sign-off
Policy changes are proposed, evaluated, and surfaced for operator approval. Auto-promotion is opt-in per agent class.
Want the deep
architecture write-up?

We'll send the technical brief — data model, policy update flow, evaluation harness, tenancy guarantees — under NDA to design partners.

Request the brief → Back to Seam