Agent Memory

AI agents have quickly become the dominant way large language models interact with users, systems, and workflows, but with their limited context windows users are required to repeat lengthy prompts every session. And as agents move from prototypes into real workflows, the lack of persistent memory becomes one of the most expensive problems teams face. In Agent Memory, Ben Labaschin, head of AI and engineering at Workhelix, shows you what it actually takes to build agents that remember: what they should store, how to retrieve it reliably, and how to design the infrastructure that keeps memory accurate and useful over time.

You'll work through the real engineering challenges that come with treating memory as part of the production stack, such as defining what's worth remembering, keeping retrieval reliable as history grows, and integrating memory with the orchestration, checkpoints, and recovery logic your agents already depend on. The result is a blueprint for memory systems that can be monitored, governed, repaired, and trusted—not just in demos, but at scale.

• Distinguish memory, context, state, and model knowledge in production systems
• Decide what agents should write, retrieve, update, summarize, and forget
• Build durable memory pipelines for long-running and resumable agent workflows
• Evaluate retrieval quality, memory drift, and failure modes as history accumulates
• Govern memory with retention policies, privacy controls, and observability
• Detect, repair, and contain stale, incorrect, or harmful memory before it compounds