Feature | Cognitive Infrastructure for AI Agents

Confidence Scoring

Your fleet knows what to trust

Every piece of knowledge carries a score that updates from real outcomes. When a finding leads to a good decision, it gets stronger. When it doesn't, it degrades. Agents don't treat a stale guess the same as a validated pattern.

Updates from real production outcomes

Per-entry confidence scores

Automatic degradation over time

Query by confidence threshold

No manual curation required

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Shared Knowledge

What one agent learns
every agent can build on

Findings, decisions, and patterns write to a shared store the moment they're created. Any agent in the fleet can read them, build on them, or challenge them.

Cross-agent reads with full lineage

Works across frameworks and models

Conflict detection built in

Millisecond read latency

Scoped access per agent or team

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Rooms

Agents that coordinate
before they act.

Agents declare what they're working on, negotiate conflicts, and commit only when aligned. Full discussion log and audit trail per decision.

Agents declare intent before acting

Conflict detection and resolution

Negotiation protocol built in

Full discussion log

Audit trail per decision

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Decision Traces

You always know
why your agents acted.

Every decision is recorded with what was read, what was weighted, what was chosen, what happened after. Queryable, exportable, audit-ready.

Full causal chain per decision

Queryable via explain()

Links outcome back to knowledge source

Exportable for audit

Auto-generates SFT/DPO training data

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Version Control

Versioned Knowledge

Every write is versioned. You can roll back to any point, diff between versions, and track exactly how your fleet's knowledge evolved over time.

Full version history per entry

Branch and merge support

Rollback to any point in time

Diff between any two versions

Git-like timeline across the fleet

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Training Signal

Production behavior
becomes improvement signal.

Decision traces auto-generate SFT and DPO datasets. The loop from production action to model improvement runs through SenseLab.

Auto-generated from decision traces

SFT and DPO dataset formats

Real production decisions, not synthetic data

Exportable to your training pipeline

Closes the loop from action to improvement

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Confidence Scoring

Your fleet knows what to trust

Updates from real production outcomes

Per-entry confidence scores

Automatic degradation over time

Query by confidence threshold

No manual curation required

Start Free

Explore Features

Shared Knowledge

What one agent learns
‍‍every agent can build on

Findings, decisions, and patterns write to a shared store the moment they're created. Any agent in the fleet can read them, build on them, or challenge them.

Cross-agent reads with full lineage

Works across frameworks and models

Conflict detection built in

Millisecond read latency

Scoped access per agent or team

Start Free