Autonomous agents require robust memory architectures to maintain context over long-running tasks. Without persistent memory, an agent is amnesic, forgetting its past actions and repeating mistakes.
The Four-Tier Memory Architecture
| Memory Type | Technology | Purpose |
|---|---|---|
| Working Memory | LLM Context Window | Immediate task execution and step-by-step reasoning. |
| Episodic Memory | Redis / Key-Value | Session state, past tool outputs, and conversational history. |
| Semantic Memory | Vector DB (Pinecone, Milvus) | RAG, enterprise knowledge base, and factual recall. |
| Procedural Memory | System Prompts | Agent instructions, tool definitions, and guardrails. |
Handling Context Overflow
A common failure mode is context overflow, where verbose tool outputs fill the context window. A single database query result or a raw web page fetch can consume thousands of tokens, crowding out the reasoning space the model needs to plan its next step.
Context Compression Strategies
- Summarizer Agent: A secondary, cheaper LLM call compresses verbose tool outputs into a dense summary before injecting them into the main agent's working memory.
- Sliding Window Truncation: Drop the oldest conversational turns once a token budget is hit, optionally retaining a running summary of what was dropped.
- Selective Retrieval: Instead of replaying full history, retrieve only the k most relevant past turns or tool results via embedding similarity.
Designing Episodic Memory
Episodic memory stores the agent's lived experience — what tools it called, what arguments it used, and what the result was. This is distinct from semantic memory: it's not general knowledge, it's a log of this specific session or task run.
class EpisodicMemory:
def __init__(self, redis_client, session_id, ttl_seconds=3600):
self.redis = redis_client
self.key = f"agent:session:{session_id}"
self.ttl = ttl_seconds
def record_step(self, tool_name, args, result):
entry = {"tool": tool_name, "args": args, "result": result}
self.redis.rpush(self.key, json.dumps(entry))
self.redis.expire(self.key, self.ttl)
def recent_steps(self, n=10):
raw = self.redis.lrange(self.key, -n, -1)
return [json.loads(r) for r in raw]Semantic Memory and Retrieval Quality
Semantic memory backed by a vector database gives agents recall over enterprise knowledge that exceeds any context window. But retrieval quality directly bounds agent quality — irrelevant chunks injected into the prompt degrade reasoning as much as missing information does.
- Chunking Strategy: Semantic chunking (splitting on topic boundaries) outperforms fixed-size chunking for agent recall, since it keeps a complete idea in one retrievable unit.
- Metadata Filtering: Tag vectors with source, timestamp, and permission scope so retrieval can pre-filter before similarity search, avoiding stale or unauthorized results.
- Hybrid Search: Combine dense vector similarity with sparse keyword (BM25) search to catch exact-match terms like product SKUs or error codes that embeddings alone can miss.
Persisting State Across Sessions
For agents that operate over days or weeks — a coding agent tracking a multi-file refactor, or a research agent building a report — state must survive process restarts. This means checkpointing not just conversation history but the agent's plan, completed sub-tasks, and intermediate artifacts to durable storage, keyed by a resumable task ID.