In agentic systems where reasoning and decision-making are part of the critical path, memory management is far more complex than just keeping a transcript of user and agent exchanges.
As impressive as modern LLMs are, their behavior is highly sensitive to the exact words, sentence structures, and even subtle omissions or inclusions in the prompt. If not carefully controlled, this can lead to prompt drift, where the agent’s behavior subtly diverges from its intended path over time.
Segregate Reasoning, Planning, and Acting
The first step is architectural: don’t leave reasoning, planning, and acting to a single monolithic agent. Even if the same model is used for all steps, the orchestration and state management must be externalized and carefully controlled. Let the agent execute, but let the framework govern how and when those executions happen.
Prompt Hygiene is Critical
Prompts for each step must be constructed with surgical precision. System messages or goals from previous steps – if blindly re-included -can throw the agent into a tailspin. However, stripping away all prior context causes the agent to lose continuity.
The trick is to selectively carry forward only what’s relevant, often in distilled or abstracted form.
Persistent Entities and Shared Context
Certain entities (e.g., user goals, constraints, defined terms) must remain constant across all steps. If the agent is allowed to reinterpret or mutate them mid-flow, the entire outcome may become invalid or unpredictable.
Think of these as immutable “anchor facts” that must be injected consistently across reasoning and action steps.
Retrospection and Long-Term Memory
There are times when the agent must look back, not just at the current flow, but at previous sessions to extract insight or learn from its history. That’s where long-term memory, historical indexing, or embedding-based retrieval come into play.
But again, the retrieved context must be filtered and curated. Dumping an entire transcript into the prompt won’t help and will likely hurt performance.
One Strategy That Works
One approach I’ve found helpful is using a second agent whose sole job is to summarize and synthesize memory for the main agent. This memory agent distills recent interactions, and we prepend that summary to the prompt, along with invariant entities at the top.
This isn’t a silver bullet but in practice, this combo has significantly improved agent stability, coherence, and performance over longer workflows.