Building Agentic Systems with Memory: A Comprehensive Guide
In this blog post, we’ll explore how to create agentic systems that utilize memory to enhance user interactions and decision-making. By harnessing both episodic and semantic memory, we can develop intelligent agents that adapt over multiple sessions, providing a more personalized experience.
Understanding Memory Structures in Agentic Systems
Episodic Memory: Capturing Specific Experiences
Episodic memory is crucial for allowing agents to store individual user interactions. This memory structure captures specific experiences, including the state, action taken, and outcome, thereby enabling the agent to recall past interactions.
python
class EpisodicMemory:
def init(self, capacity=100):
self.capacity = capacity
self.episodes = []
def store(self, state, action, outcome):
...Semantic Memory: Generalizing Patterns Over Time
Semantic memory allows agents to learn long-term patterns from interactions. It helps in keeping track of user preferences and overall success rates, enabling the agent to make better choices based on learned data.
python
class SemanticMemory:
def init(self):
self.preferences = defaultdict(float)
…
Designing the Agent’s Perception and Decision-Making
Perceiving User Intent
The agent’s ability to understand user input is essential. By detecting keywords, the system can discern whether the user seeks recommendations or intends to update preferences.
python
def perceive(self, user_input):
…
Planning Based on Memory
Once intent is understood, the agent leverages memories stored in both the episodic and semantic frameworks to formulate a plan of action.
python
def plan(self, state):
…
Executing Actions and Learning from Feedback
Acting on User Requests
Agents implement actions based on the planned responses. This may involve recommending content or updating user preferences based on new information.
python
def act(self, action):
…
Reflecting and Revising Plans
Feedback from users is critical. The agent reflects on its actions by storing experiences in episodic memory and refining future plans based on success or failure.
python
def reflect(self, state, action, outcome, success):
…
Running Multiple Sessions to Enhance Autonomy
Through simulation of user interactions, we witness the agent’s growth in personalization. By executing the perceive → plan → act → reflect loop, agents adapt to user preferences over time.
python
def run_session(self, user_inputs):
…
Evaluating Memory Usage
It’s important to analyze how well the agent utilizes its memories. By assessing stored episodes and learned patterns, we gain insight into the agent’s evolution.
python
def evaluate_memory_usage(agent):
…
Conclusion
In conclusion, integrating both episodic and semantic memory allows us to build adaptive agents capable of improving their recommendations and actions over time. This study illustrates the profound impact of memory-driven decision-making in achieving long-term autonomy in agentic systems.
Related Keywords: Agentic Systems, Memory Structures, Episodic Memory, Semantic Memory, Intelligent Agents, User Interaction, Autonomous Learning.
