AgenticMemory
What happens when an AI agent can remember, reason about its reasoning, and correct itself across sessions?
It's Tuesday. Your production monitoring triggers an alert: the payment service is returning 500 errors at a rate of 12% of all requests. You engage the agent.
Session 1: Triage (10:00 AM)
The agent records initial facts: "Payment service 500 error rate: 12%", "No deployments in the last 24 hours." It creates an Inference: "Likely not a code regression" (confidence 0.72, CAUSED_BY the "no deployments" fact). It finds a Skill node from 3 months ago: "When payment errors spike without deployment, check Stripe API status first." Stripe status: operational. Second Inference: "If not code and not Stripe, likely database or connection issue" (confidence 0.65). Decision: "Investigate database connection pool next."
Session 2: Investigation (11:30 AM)
Memory loads in 3.7 milliseconds. New facts from database metrics: "Connection pool utilization: 198/200 (99%)", "Average query time: 340ms (normal: 45ms)", "3 queries > 5 seconds." New Inference: "Connection pool exhaustion caused by slow queries" (confidence 0.88). A Correction supersedes the vaguer inference from session 1. Decision: "Add 30s query timeout, increase pool to 400, identify slow queries."
Session 3: Remediation (2:00 PM)
Outcome facts: "Pool increased to 400 — error rate dropped to 2%", "3 slow queries identified." The agent finds a semantically similar Skill from 2 months ago via memory_similar (9ms, similarity 0.87): "Aggregation queries on large tables benefit from materialized views." Decision: "Optimize the 3 slow queries."
Session 4: Postmortem (Wednesday)
The agent loads all sessions. memory_query finds 4 Decisions, 1 Correction, 11 Facts. memory_causal traces the complete dependency tree from the initial alert: 11 facts → 4 inferences → 3 corrections → 4 decisions → 1 skill applied. Belief revision with the hypothetical "slow query monitoring was in place" identifies 2 decisions that would have been unnecessary. The postmortem is recorded as an Episode, with a new Skill: "Add slow query alerting to prevent connection pool cascades."
Postmortem complete. Root cause chain: 3 slow queries → connection pool exhaustion → 12% error rate. Resolution: pool increase (immediate), query timeout (mitigation), query optimization (permanent). New skill recorded: slow query alerting prevents cascade. Belief revision shows this could have been a 30-minute fix with proper monitoring.
Four sessions. Seventeen memory nodes. Twenty-three edges. Every decision traceable to evidence, every correction preserving history, every skill reusable in future incidents. The agent didn't just solve the problem — it built institutional knowledge that makes the next incident faster.
In plain terms
This is the difference between an amnesiac firefighter who forgets each blaze and a seasoned veteran who remembers every incident, knows which patterns repeat, and gets faster with every call. AgenticMemory turns throwaway conversations into compounding institutional intelligence.