Agent Recall and Planning: The Retrieval Loop
Discover how agents use vector databases for planning and decision-making through recursive retrieval loops and past-action analysis.
Agent Recall and Planning: The Retrieval Loop
An agent doesn't just "Search" the database once. To solve complex, multi-step problems, an agent uses its long-term memory to Plan. When an agent hits a roadblock, it "Recalls" how it solved a similar roadblock in the past.
In this lesson, we explore the Retrieval-Augmented Planning loop.
1. The "Think-Search-Act" Cycle
Traditional RAG is: Question -> Search -> Answer.
Agentic Recall is: Task -> Recall similar plans -> Refine Plan -> Act -> Observe -> Store Result.
By storing Past Plans and their Outcomes in a vector database, an agent becomes self-correcting.
2. Storing "Action Success" vectors
When an agent performs a task, you shouldn't just store the dialogue. You should store the Execution Metadata.
- Input: "How to fix a 404 error on a Python Flask app."
- Action Taken: "Checked the routes.py file for a missing leading slash."
- Outcome: SUCCESS (+1.0 score).
Retrieval Profit: Next time the agent sees a 404 error, it queries its memory for "404" AND filters for score > 0.8. It immediately knows to check the leading slashes first.
3. Implementation: Multi-Query Planning (Python)
Sometimes the initial query isn't enough to find the right memory. We use Query Expansion.
def expand_and_recall(agent_task, collection):
# 1. Use a cheap LLM to generate 3 variation of the memory query
# "How to fix a laptop" -> ["past laptop repairs", "hardware troubleshooting", "power issue fixes"]
queries = generate_variations(agent_task)
memories = []
for q in queries:
results = collection.query(query_texts=[q], n_results=1)
memories.extend(results['documents'])
return list(set(memories)) # Deduplicate
4. Visualizing the Decision Flow
graph TD
A[New Task] --> B[Retrieve Similar Past Plans]
B --> C{Success Found?}
C -->|Yes| D[Adapt Past Plan]
C -->|No| E[Generate New Plan]
D --> F[Execute]
E --> F[Execute]
F --> G[Observe Outcome]
G --> H[Store Outcome in Vector DB]
5. Summary and Key Takeaways
- Memory for Logic: Agents use vector DBs to recall logic paths, not just data points.
- Success Weighting: Store the outcome of actions as metadata to guide future decisions.
- Query Expansion: If the agent is stuck, have it generate new search queries for its own memory.
- The Loop: Always store the result of a plan back into the database to create a continuous learning cycle.
In the next lesson, we’ll address the "Accumulation Problem": Memory Expiration Strategies.