Previous: Decision Home Back to Patterns

Agentic Learning Patterns

Patterns for continuous learning and adaptation in agentic systems

These patterns represent the cutting edge of learning mechanisms in modern agentic systems. While traditional machine learning patterns focus on model training and optimization, these patterns address how agents can learn and adapt in real-time, leveraging the unique capabilities of large language models and modern AI architectures. They are essential for creating truly intelligent and adaptive agentic systems.

In-Context Learning

Description

A learning pattern where the agent adapts its behavior based on examples provided in the current context or prompt, without requiring parameter updates. This pattern leverages the inherent capabilities of large language models to learn from examples in real-time, making it particularly valuable for rapid adaptation and task-specific learning.

Key Components

  • Prompt Examples

    • Carefully crafted examples that demonstrate the desired behavior
    • Can include both input-output pairs and step-by-step reasoning
    • Examples are provided in the context window of the model

  • Zero-shot Learning

    • Ability to perform tasks without explicit examples
    • Leverages model's pre-trained knowledge
    • Useful for well-defined, common tasks

  • Few-shot Learning

    • Learning from a small number of examples (typically 1-5)
    • Balances between zero-shot and many-shot approaches
    • Ideal for task-specific adaptation

  • Chain-of-Thought

    • Step-by-step reasoning process
    • Makes the learning process transparent
    • Enables complex problem-solving

Implementation # for In-Context Learning 

examples = [
    {"input": "Translate 'Hello' to Spanish.", "output": "Hola."},
    {"input": "What is 2 + 2?", "output": "4."}
]

def generate_prompt(task, examples):
    prompt = ""
    for ex in examples:
        prompt += f"Input: {ex['input']}\nOutput: {ex['output']}\n"
    prompt += f"Input: {task}\nOutput:"
    return prompt

user_task = "Translate 'Goodbye' to French."
prompt = generate_prompt(user_task, examples)
llm_response = llm_agent.complete(prompt)
print(llm_response)

Implementation # for Meta-Learning 

class MetaLearner:
    def __init__(self):
        self.strategies = ["few-shot", "zero-shot"]
        self.history = []
    def select_strategy(self, task):
        # Analyze task and history to pick best strategy
        return "few-shot" if task.has_examples else "zero-shot"
    def learn(self, task):
        strategy = self.select_strategy(task)
        result = llm_agent.apply_strategy(strategy, task)
        self.history.append({"task": task, "strategy": strategy, "result": result})
        return result

task = Task(content="Translate 'Hello' to German.", has_examples=True)
meta_learner = MetaLearner()
result = meta_learner.learn(task)
print(result)

Implementation # for Self-Refinement 

def self_refine(task, max_iterations=3, threshold=0.8):
    output = llm_agent.complete(task)
    for i in range(max_iterations):
        critique = llm_agent.critique(task, output)
        if critique["score"] >= threshold:
            break
        output = llm_agent.improve(task, output, critique)
    return output

result = self_refine("Summarize the benefits of renewable energy.")
print(result)

Implementation # for Memory-Augmented Learning 

class Memory:
    def __init__(self):
        self.memories = []
    def store(self, experience):
        self.memories.append(experience)
    def retrieve(self, query):
        # Return most relevant memories (simple match for demo)
        return [m for m in self.memories if query in m["content"]]

memory = Memory()
memory.store({"content": "Agent completed task A."})
memory.store({"content": "User reported error on task B."})

query = "task A"
relevant = memory.retrieve(query)
print(relevant)

Meta-Learning

Description

A learning pattern where the agent develops the ability to quickly adapt to new tasks with minimal data by learning effective learning strategies. This pattern enables agents to become more efficient learners over time, reducing the need for extensive training data for new tasks.

Key Components

  • Task Distribution

    • Collection of diverse learning scenarios
    • Variety of task types and complexities
    • Balanced representation of different domains

  • Learning Strategy

    • Methods for adapting to new tasks
    • Optimization of learning parameters
    • Selection of appropriate learning approaches

  • Quick Adaptation

    • Rapid task mastery with minimal data
    • Efficient transfer of learned strategies
    • Optimization of learning speed

  • Meta-parameters

    • Learning rate adaptation
    • Strategy selection criteria
    • Performance optimization parameters

Implementation 

# Python Pseudocode for Meta-Learning

class MetaLearner:
    def __init__(self):
        self.strategies = ["few-shot", "zero-shot"]
        self.history = []
    def select_strategy(self, task):
        # Analyze task and history to pick best strategy
        return "few-shot" if task.has_examples else "zero-shot"
    def learn(self, task):
        strategy = self.select_strategy(task)
        result = llm_agent.apply_strategy(strategy, task)
        self.history.append({"task": task, "strategy": strategy, "result": result})
        return result

task = Task(content="Translate 'Hello' to German.", has_examples=True)
meta_learner = MetaLearner()
result = meta_learner.learn(task)
print(result)

Related Patterns

  • In-Context Learning: For example-based learning
  • Self-Refinement: For performance improvement
  • Memory-Augmented Learning: For experience storage

Self-Refinement

Description

A learning pattern where the agent improves its performance by analyzing its own outputs and iteratively refining its approach through self-critique and adjustment. This pattern enables continuous improvement and quality enhancement without external supervision.

Key Components

  • Self-Analysis

    • Evaluation of output quality
    • Identification of improvement areas
    • Assessment of reasoning process

  • Feedback Loop

    • Iterative improvement process
    • Continuous quality enhancement
    • Adaptive refinement strategies

  • Critique

    • Performance assessment
    • Error identification
    • Quality metrics evaluation

  • Adjustment

    • Strategy modification
    • Parameter optimization
    • Behavior refinement

Implementation 

# Python Pseudocode for Self-Refinement

def self_refine(task, max_iterations=3, threshold=0.8):
    output = llm_agent.complete(task)
    for i in range(max_iterations):
        critique = llm_agent.critique(task, output)
        if critique["score"] >= threshold:
            break
        output = llm_agent.improve(task, output, critique)
    return output

result = self_refine("Summarize the benefits of renewable energy.")
print(result)

Related Patterns

  • In-Context Learning: For example-based learning
  • Meta-Learning: For learning strategies
  • Memory-Augmented Learning: For experience storage

Memory-Augmented Learning

Description

A learning pattern where the agent uses external memory systems to store and retrieve past experiences, enabling continual learning without requiring model retraining. This pattern is crucial for maintaining context and leveraging historical knowledge in agentic systems.

Key Components

  • Vector Database

    • Efficient storage of experience vectors
    • Semantic search capabilities
    • Scalable memory management

  • Retrieval

    • Context-aware memory access
    • Relevance-based retrieval
    • Dynamic memory selection

  • Episodic Memory

    • Event-based experience storage
    • Temporal sequence tracking
    • Context preservation

  • Semantic Memory

    • Knowledge-based storage
    • Concept organization
    • Relationship mapping

Implementation 

# Python Pseudocode for Memory-Augmented Learning

class Memory:
    def __init__(self):
        self.memories = []
    def store(self, experience):
        self.memories.append(experience)
    def retrieve(self, query):
        # Return most relevant memories (simple match for demo)
        return [m for m in self.memories if query in m["content"]]

memory = Memory()
memory.store({"content": "Agent completed task A."})
memory.store({"content": "User reported error on task B."})

query = "task A"
relevant = memory.retrieve(query)
print(relevant)

Related Patterns

  • In-Context Learning: For example-based learning
  • Meta-Learning: For learning strategies
  • Self-Refinement: For performance improvement