Introduction
In this tutorial, we'll build a cognitive blueprint-driven runtime agent framework that enables AI agents to plan, execute, validate, and improve their outputs over time. This approach is inspired by the latest advancements in agentic AI systems, where agents maintain structured memory, follow defined goals, and validate their actions against cognitive blueprints.
By the end of this tutorial, you'll have built a modular agent framework that can:
- Define and maintain cognitive blueprints
- Store and retrieve memory across interactions
- Plan and execute actions based on goals
- Validate outputs against predefined criteria
This framework serves as a foundation for next-generation agentic AI systems that can learn and improve from their interactions.
Prerequisites
To follow along with this tutorial, you should have:
- Basic understanding of Python programming
- Intermediate knowledge of object-oriented programming concepts
- Installed Python 3.8 or higher
- Basic understanding of AI/ML concepts and LLMs (Large Language Models)
Optional but recommended:
- Familiarity with JSON and data structures
- Experience with basic prompt engineering
Step-by-Step Instructions
1. Setting up the Project Structure
We'll start by creating a project structure that separates concerns and makes our code maintainable.
agent_framework/
├── __init__.py
├── agent.py
├── blueprint.py
├── memory.py
├── planner.py
├── validator.py
└── tools.py
This structure allows us to modularize each component of our cognitive agent system.
2. Creating the Cognitive Blueprint Class
The blueprint defines the agent's identity, goals, planning approach, and validation criteria. Let's create the blueprint class:
class CognitiveBlueprint:
def __init__(self, identity, goals, planning_approach, validation_criteria):
self.identity = identity # Agent's role and characteristics
self.goals = goals # List of objectives
self.planning_approach = planning_approach # Planning strategy
self.validation_criteria = validation_criteria # How outputs are validated
def to_dict(self):
return {
"identity": self.identity,
"goals": self.goals,
"planning_approach": self.planning_approach,
"validation_criteria": self.validation_criteria
}
@classmethod
def from_dict(cls, data):
return cls(
data["identity"],
data["goals"],
data["planning_approach"],
data["validation_criteria"]
)This blueprint class stores all the essential information that guides our agent's behavior. The identity helps define the agent's role, while goals and planning approach determine what it should do and how it should do it.
3. Implementing the Memory System
A key component of agentic AI is the ability to remember past interactions and learn from them:
import json
from datetime import datetime
class Memory:
def __init__(self):
self.memory_store = []
def add_memory(self, content, context=""):
memory_entry = {
"timestamp": datetime.now().isoformat(),
"content": content,
"context": context
}
self.memory_store.append(memory_entry)
def get_memory(self, limit=5):
return self.memory_store[-limit:]
def save_to_file(self, filename):
with open(filename, 'w') as f:
json.dump(self.memory_store, f)
def load_from_file(self, filename):
with open(filename, 'r') as f:
self.memory_store = json.load(f)The memory system stores interactions and can be persisted to disk. This allows agents to learn from past experiences and improve over time.
4. Building the Planner Component
The planner component breaks down goals into actionable steps:
class Planner:
def __init__(self, blueprint):
self.blueprint = blueprint
def plan(self, task):
plan = {
"task": task,
"steps": [],
"planning_approach": self.blueprint.planning_approach
}
# Simple planning logic based on blueprint
if self.blueprint.planning_approach == "step_by_step":
plan["steps"] = self._step_by_step_plan(task)
elif self.blueprint.planning_approach == "goal_oriented":
plan["steps"] = self._goal_oriented_plan(task)
return plan
def _step_by_step_plan(self, task):
return [f"Step 1: Understand {task}", f"Step 2: Execute {task}", f"Step 3: Validate {task}"]
def _goal_oriented_plan(self, task):
return [f"Goal: {task}", "Break down into sub-goals", "Execute each sub-goal"]This planner component uses the blueprint's planning approach to determine how to break down tasks, enabling agents to adapt their planning strategies based on their cognitive blueprint.
5. Creating the Validator Component
Validation ensures that outputs meet the agent's defined criteria:
class Validator:
def __init__(self, blueprint):
self.blueprint = blueprint
def validate(self, output):
criteria = self.blueprint.validation_criteria
# Simple validation logic
results = {}
for criterion in criteria:
if criterion == "completeness":
results[criterion] = self._check_completeness(output)
elif criterion == "accuracy":
results[criterion] = self._check_accuracy(output)
elif criterion == "relevance":
results[criterion] = self._check_relevance(output)
return results
def _check_completeness(self, output):
# Simplified completeness check
return len(output) > 10
def _check_accuracy(self, output):
# Simplified accuracy check
return True # In practice, this would involve more complex logic
def _check_relevance(self, output):
# Simplified relevance check
return TrueThe validator component ensures that agent outputs meet the standards defined in the cognitive blueprint, maintaining quality and consistency.
6. Creating the Runtime Agent
Now we'll combine all components into a runtime agent:
class RuntimeAgent:
def __init__(self, blueprint):
self.blueprint = blueprint
self.memory = Memory()
self.planner = Planner(blueprint)
self.validator = Validator(blueprint)
self.name = blueprint.identity.get("name", "Agent")
def execute_task(self, task):
# Step 1: Plan
plan = self.planner.plan(task)
self.memory.add_memory(f"Planning task: {task}", "Planning")
# Step 2: Execute
result = self._execute_plan(plan)
self.memory.add_memory(f"Executed task: {task}, Result: {result}", "Execution")
# Step 3: Validate
validation_results = self.validator.validate(result)
self.memory.add_memory(f"Validation results: {validation_results}", "Validation")
return {
"task": task,
"plan": plan,
"result": result,
"validation": validation_results
}
def _execute_plan(self, plan):
# Simple execution logic
return f"Completed {plan['task']} with steps: {plan['steps']}"
def get_memory(self):
return self.memory.get_memory()
def save_memory(self, filename):
self.memory.save_to_file(filename)
def load_memory(self, filename):
self.memory.load_from_file(filename)This runtime agent orchestrates all components, executing tasks according to the blueprint and maintaining memory of its activities.
7. Testing Our Framework
Let's test our framework with a practical example:
# Create a cognitive blueprint
blueprint = CognitiveBlueprint(
identity={"name": "Research Assistant", "role": "Research Analyst"},
goals=["Analyze market trends", "Generate report"],
planning_approach="step_by_step",
validation_criteria=["completeness", "accuracy", "relevance"]
)
# Create and run agent
agent = RuntimeAgent(blueprint)
# Execute a task
result = agent.execute_task("Analyze consumer behavior trends")
print(result)
# Check memory
print("\nMemory Entries:")
for entry in agent.get_memory():
print(entry)This example demonstrates how our framework can create an agent that follows a structured approach to task execution, maintaining memory of its activities.
Summary
In this tutorial, we've built a complete cognitive blueprint-driven runtime agent framework that demonstrates key concepts of next-generation agentic AI. Our framework includes:
- Cognitive Blueprints: Define agent identity, goals, and behavior patterns
- Memory System: Stores and retrieves interaction history
- Planner Component: Breaks down tasks according to defined approaches
- Validator Component: Ensures outputs meet quality criteria
- Runtime Agent: Orchestrates all components for task execution
This modular approach allows for easy extension and customization of agent behaviors. You can enhance this framework by integrating with LLMs, adding more sophisticated validation logic, or implementing advanced planning strategies. The framework provides a solid foundation for building more complex agentic AI systems that can learn, adapt, and improve over time.
