[ in private beta ]
Contextual Learning for
AI Agents
Spolm turns production telemetry into long-term memory for AI agents. Stop repeating mistakes. Start improving with every interaction.
Modern AI agents[1] deployed in production exhibit a critical failure mode: they repeat identical mistakes across sessions. In multi-step workflows[2], agents frequently select incorrect tools, misinterpret context, and enter unproductive loops[3].
We propose an experiential learning layer[4] that analyzes production telemetry[5] to extract decision patterns—both successful and failed—stored in a knowledge base ranked by accuracy. Before execution, agents retrieve historical interactions[6] as context, adapting strategy pre-emptively.