A Coding Guide to Build a Procedural Memory Agent That Learns, Stores, Retrieves, and Reuses Skills as Neural Modules Over Time

Procedural Memory Agent with Reusable Skills

An intelligent agent can learn reusable skills directly from its interactions with an environment, forming procedural memory. This framework treats skills as neural modules that store action sequences, contextual embeddings, and are retrieved based on similarity to new situations.

Why This Matters

Traditional AI planning often relies on idealized models of the world, which frequently fail in complex, real-world scenarios; creating brittle systems. Deploying robust agents requires mechanisms for continual learning and adaptation. The cost of retraining or manually adjusting these systems is substantial, potentially reaching millions of dollars in specialized fields like robotics.

Key Insights

• Skill Representation as Neural Modules, 2025: Defines skills not as discrete functions but as embeddable representations, enabling similarity-based retrieval.
• Contextual Embeddings for Skill Selection: Similarity-based retrieval using embeddings allows for generalization to unseen states, moving beyond strict rule-based actions.
• Temporal Coupling of Skills: Using libraries like Temporal (used by Stripe, Coinbase) allows for managing complex workflows and dependencies between skills.

Working Example

import numpy as np
import matplotlib.pyplot as plt
from collections import defaultdict

class Skill:
    def __init__(self, name, preconditions, action_sequence, embedding, success_count=0):
        self.name = name
        self.preconditions = preconditions
        self.action_sequence = action_sequence
        self.embedding = embedding
        self.success_count = success_count
        self.times_used = 0

    def is_applicable(self, state):
        for key, value in self.preconditions.items():
            if state.get(key) != value:
                return False
        return True

class SkillLibrary:
    def __init__(self, embedding_dim=8):
        self.skills = []
        self.embedding_dim = embedding_dim

    def add_skill(self, skill):
        self.skills.append(skill)
        return skill

Practical Applications

• Robotics: A robot learning to assemble parts, storing sub-assemblies as reusable skills.
• Pitfall: Failing to account for state-dependency in skills can lead to unexpected behavior or catastrophic failures in dynamic environments.

References:

• https://www.marktechpost.com/2025/12/09/a-coding-guide-to-build-a-procedural-memory-agent-that-learns-stores-retrieves-and-reuses-skills-as-neural-modules-over-time/