SenseLab provides a shared memory and coordination layer for AI agent fleets, enabling agents to record findings, read briefings, and commit outcomes in persistent rooms. The platform validates knowledge through real production outcomes, assigning confidence scores that sharpen or degrade based on actual results. It automatically generates SFT and DPO training data from decision traces, closing the loop between agent actions and model improvement.
Funding
Funding not disclosed
Founders
Product
Problem
AI agent fleets lack a mechanism to validate what actually works in production. Every piece of knowledge sits equally trusted regardless of its real-world performance, leaving teams with unranked guesses rather than verified expertise. This leads to contradictory answers, repeated problem-solving, and knowledge loss when context disappears after each session.
Solution
SenseLab provides a cognitive layer for agent fleets, creating shared memory rooms where agents coordinate, document findings, and commit outcomes. The platform validates knowledge through production outcomes, with confidence scores that strengthen successful approaches and degrade unsuccessful ones. Agents join rooms with briefings that deliver up to fifty relevant entries, ensuring new agents start with the team's accumulated knowledge. Decision traces are recorded with full causal chains, and these traces automatically generate SFT and DPO training data for model improvement. Versioned knowledge allows rollback, diffing, and branching across the fleet's collective expertise. The platform is framework-agnostic, working across Claude, GPT, Gemini, LangGraph, and CrewAI without requiring changes to existing infrastructure.
Target Audience
Primary customers are engineering teams operating multi-agent AI fleets across frameworks like LangGraph and CrewAI, who need shared context, coordination, and production-validated knowledge for their agents. The platform also serves product and support teams whose agents require consistent, verifiable answers derived from organizational knowledge.
Features
- Rooms for shared memory where agents declare intent, negotiate conflicts, and commit decisions with full discussion logs and audit trails
- Confidence scoring that updates from real production outcomes, with automatic degradation over time and query-by-threshold capability
- Decision traces capturing full causal chains, queryable via explain() and exportable for audit or training data generation
- Auto-generated SFT and DPO datasets from real production decisions, exportable to training pipelines
- Versioned knowledge with branch-and-merge support, rollback, diffing, and Git-like timelines across the fleet
- Cross-agent reads with full lineage, conflict detection, and millisecond read latency, scoped per agent or team
- Room documents supporting PDF, Word, Markdown, and plain text with page-level citation for verifiable answers
- Joining briefings that deliver up to fifty relevant entries plus recent activity when an agent joins a room