Architecting the Agent Mind
Explore the engineering principles behind GenAI context management. Navigate the trade-offs between ephemeral Sessions and persistent Memory to build robust, hallucination-resistant agents.
Sessions
Short-term interaction history. Fast, expensive, and limited by context windows.
Memory
Long-term storage. Structured, scalable, and essential for personalization.
⚡ Sessions & Context Windows
Sessions capture the immediate "Short-term Memory" of an agent. The critical engineering challenge here is managing the Context Window. As conversations grow, full history becomes prohibitively expensive and prone to "Lost in the Middle" phenomena.
Interact below to compare strategies for handling long context conversations.
Context Strategy
Engineering Note
Full History leads to exponential token costs. Optimization is mandatory for production systems.
Token Usage vs. Conversation Turns
💾 The Memory Bank
To move beyond transient sessions, agents require Memory. The report identifies several distinct types of memory modeled after human cognition. Each requires a specific storage architecture.
Select a memory type to reveal its architectural requirements.
Episodic Memory
🕰️Past experiences and events.
Semantic Memory
📚Facts, concepts, and world knowledge.
Procedural Memory
🛠️Implicit knowledge on "how to do" things.
Episodic Memory
Experience BasedDefinition
Stores sequences of events and interactions. It allows the agent to recall what happened in previous turns or sessions, providing continuity.
Storage Architecture
Vector Database + Time-Decay Weighting
Often retrieved via semantic similarity search biased by recency.
Example in Action
"User: 'Remember when we discussed Python decorators?'
Agent: 'Yes, in our session last Tuesday, we covered the @property decorator...'"
Memory Generation Pipeline
Memories don't just appear; they must be manufactured. The report outlines a pipeline to transform raw unstructured text into structured, queryable data.
1. Raw Input
User prompts & conversation history.
2. Extraction
Identify entities, intent, and facts.
3. Consolidation
De-duplicate & merge with existing graphs.
4. Storage
Vector Embeddings & Knowledge Graphs.
Select a stage above
Click on any step in the pipeline to understand the specific engineering tasks involved.