Architecting Persistent Memory for Multi-Agent Systems

In production multi-agent systems, memory determines whether agents behave like a coordinated team or an expensive collision of independent processes. 

Focusing on agent communication with better prompts, clearer delegation, and message-passing isn’t enough, as agents struggle to maintain a shared understanding of what’s already occurred, what’s currently true, and any decisions that have already been made. Without shared state, agents re-retrieve the same context, execute redundant operations, and propagate inconsistencies downstream, burning through token budgets at up to 15x the cost of single-agent interactions. (Source: Anthropic)

Join Adam B. Struck (CEO & Co-Founder of Memori Labs) and David Joy (Senior Manager, Sales Engineering at Cockroach Labs) to understand the architectural decisions behind memory infrastructure for multi-agent systems. See how shared context is structured and retrieved across agent sessions, providing an LLM-agnostic persistent memory layer that gives every agent in your system a common source of truth—all backed by a globally distributed, transactionally consistent database designed to handle the high-throughput, high-availability data infrastructure that production agent memory demands.

You’ll learn:

• Why shared agent memory is the foundation for multi-agent systems in production

• How Memori Labs enables shared, persistent context across LLMs and agents without vendor lock-in

• Why CockroachDB's distributed data architecture is the ideal backbone for agent memory at scale, handling the high-throughput, always-on data infrastructure that agentic AI demands

• How multi-agent systems actually remember, coordinate, and scale beyond just agent communication