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Chapter 2: Agent Architecture |
SuperAGI Tutorial |
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Welcome to Chapter 2: Agent Architecture. In this part of SuperAGI Tutorial: Production-Ready Autonomous AI Agents, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.
Understand SuperAGI's agent design patterns, reasoning engines, and the components that enable autonomous behavior.
SuperAGI's agent architecture is built on sophisticated design patterns that enable agents to reason, plan, and execute complex tasks autonomously. This chapter explores the core components, reasoning mechanisms, and architectural decisions that make SuperAGI agents powerful.
from superagi.agent import Agent
from superagi.core import ReasoningEngine, ExecutionEngine, PlanningModule
class AgentArchitecture:
"""
SuperAGI Agent Architecture Overview
Components:
├── Reasoning Engine - Decision making and logic
├── Planning Module - Goal decomposition and strategy
├── Execution Engine - Action implementation
├── Memory System - Context and experience storage
├── Tool Manager - External capability integration
└── Feedback Loop - Learning and adaptation
"""
def __init__(self, config: dict):
self.reasoning = ReasoningEngine(config.get("reasoning", {}))
self.planning = PlanningModule(config.get("planning", {}))
self.execution = ExecutionEngine(config.get("execution", {}))
self.memory = MemorySystem(config.get("memory", {}))
self.tools = ToolManager(config.get("tools", []))
def process_goal(self, goal: str):
"""Process a goal through the agent architecture."""
# 1. Understand the goal
understanding = self.reasoning.analyze(goal)
# 2. Create a plan
plan = self.planning.create_plan(understanding)
# 3. Execute with feedback
for step in plan.steps:
result = self.execution.execute(step, self.tools)
self.memory.store(step, result)
self.reasoning.evaluate(result)
return self.reasoning.synthesize_results()stateDiagram-v2
[*] --> Initialization: Create Agent
Initialization --> Idle: Setup Complete
Idle --> GoalReceived: New Goal
GoalReceived --> Reasoning: Analyze Goal
Reasoning --> Planning: Understand Context
Planning --> Executing: Plan Created
Executing --> ToolUse: Need External Action
ToolUse --> Executing: Tool Result
Executing --> Evaluating: Step Complete
Evaluating --> Executing: Continue Plan
Evaluating --> Learning: Plan Complete
Learning --> Idle: Update Memory
Executing --> ErrorHandling: Execution Failed
ErrorHandling --> Planning: Replan
ErrorHandling --> Idle: Unrecoverable
class ReasoningEngine:
"""Engine for agent reasoning and decision making."""
def __init__(self, llm_config: dict):
self.llm = self._initialize_llm(llm_config)
self.reasoning_strategies = {
"chain_of_thought": self._cot_reasoning,
"tree_of_thought": self._tot_reasoning,
"react": self._react_reasoning,
}
def reason(self, context: dict, strategy: str = "chain_of_thought") -> dict:
"""Apply reasoning to context."""
reasoning_fn = self.reasoning_strategies.get(strategy)
return reasoning_fn(context)
def _cot_reasoning(self, context: dict) -> dict:
"""Chain of thought reasoning."""
prompt = f"""
Given the following context, reason step by step.
Goal: {context['goal']}
Current State: {context['state']}
Available Tools: {context['tools']}
Previous Actions: {context['history']}
Think through this step by step:
1. What is the current situation?
2. What needs to be done next?
3. What tool or action is most appropriate?
4. What are the potential outcomes?
5. What is the best decision?
Reasoning:
"""
response = self.llm.generate(prompt)
return self._parse_reasoning(response)
def _react_reasoning(self, context: dict) -> dict:
"""ReAct (Reasoning + Acting) pattern."""
prompt = f"""
Goal: {context['goal']}
Observation: {context['observation']}
Based on the observation, provide:
Thought: [Your reasoning about what to do next]
Action: [The action to take]
Action Input: [Input for the action]
"""
response = self.llm.generate(prompt)
return self._parse_react(response)
def _tot_reasoning(self, context: dict) -> dict:
"""Tree of thought reasoning for complex problems."""
# Generate multiple reasoning paths
paths = []
for _ in range(3):
path = self._generate_reasoning_path(context)
paths.append(path)
# Evaluate paths
scored_paths = [(self._evaluate_path(p), p) for p in paths]
best_path = max(scored_paths, key=lambda x: x[0])[1]
return {"reasoning_path": best_path, "all_paths": paths}class DecisionMaker:
"""Make decisions based on reasoning output."""
def __init__(self, config: dict):
self.confidence_threshold = config.get("confidence_threshold", 0.7)
self.max_alternatives = config.get("max_alternatives", 3)
def decide(self, reasoning_output: dict, available_actions: list) -> dict:
"""Make a decision based on reasoning."""
# Score each possible action
scored_actions = []
for action in available_actions:
score = self._score_action(action, reasoning_output)
scored_actions.append((score, action))
# Sort by score
scored_actions.sort(key=lambda x: x[0], reverse=True)
# Select best action if confident
best_score, best_action = scored_actions[0]
if best_score >= self.confidence_threshold:
return {
"decision": best_action,
"confidence": best_score,
"alternatives": scored_actions[1:self.max_alternatives + 1]
}
# If not confident, return for human review or more reasoning
return {
"decision": None,
"requires_clarification": True,
"candidates": scored_actions[:self.max_alternatives]
}
def _score_action(self, action: dict, reasoning: dict) -> float:
"""Score an action based on reasoning context."""
score = 0.5 # Base score
# Alignment with goal
if self._aligns_with_goal(action, reasoning.get("goal_analysis")):
score += 0.2
# Feasibility
if self._is_feasible(action, reasoning.get("available_resources")):
score += 0.15
# Risk assessment
risk = self._assess_risk(action, reasoning.get("risk_factors"))
score -= risk * 0.15
# Historical success rate
success_rate = self._get_historical_success(action)
score += success_rate * 0.1
return min(max(score, 0), 1) # Clamp to [0, 1]class PlanningModule:
"""Module for planning and goal decomposition."""
def __init__(self, llm, config: dict):
self.llm = llm
self.max_depth = config.get("max_depth", 5)
self.max_breadth = config.get("max_breadth", 10)
def decompose_goal(self, goal: str, context: dict) -> list:
"""Decompose a high-level goal into subtasks."""
prompt = f"""
Decompose this goal into smaller, actionable subtasks.
Goal: {goal}
Context: {context}
Rules:
- Each subtask should be independently completable
- Subtasks should be ordered by dependency
- Include success criteria for each subtask
- Maximum {self.max_breadth} subtasks
Return as JSON:
{{
"subtasks": [
{{
"id": "task_1",
"description": "...",
"dependencies": [],
"success_criteria": "...",
"estimated_complexity": "low|medium|high"
}}
]
}}
"""
response = self.llm.generate(prompt)
return self._parse_subtasks(response)
def create_execution_plan(self, subtasks: list) -> dict:
"""Create an execution plan from subtasks."""
# Build dependency graph
graph = self._build_dependency_graph(subtasks)
# Topological sort for execution order
execution_order = self._topological_sort(graph)
# Identify parallelizable tasks
parallel_groups = self._identify_parallel_groups(execution_order, subtasks)
return {
"execution_order": execution_order,
"parallel_groups": parallel_groups,
"estimated_steps": len(execution_order),
"critical_path": self._find_critical_path(graph, subtasks)
}class AdaptivePlanner:
"""Planner that adapts based on execution results."""
def __init__(self, planning_module: PlanningModule):
self.planner = planning_module
self.execution_history = []
def adapt_plan(self, current_plan: dict, execution_result: dict) -> dict:
"""Adapt plan based on execution results."""
if execution_result["status"] == "success":
# Mark step as complete, continue with plan
return self._mark_complete_and_continue(current_plan)
elif execution_result["status"] == "partial":
# Adjust remaining steps
return self._adjust_remaining_steps(current_plan, execution_result)
elif execution_result["status"] == "failed":
# Replan from failure point
return self._replan_from_failure(current_plan, execution_result)
def _replan_from_failure(self, plan: dict, failure: dict) -> dict:
"""Create new plan accounting for failure."""
context = {
"original_goal": plan["goal"],
"completed_steps": self._get_completed_steps(plan),
"failed_step": failure["step"],
"failure_reason": failure["reason"],
"available_alternatives": self._find_alternatives(failure)
}
# Generate alternative approaches
alternatives = self.planner.decompose_goal(
f"Alternative approach for: {failure['step']['description']}",
context
)
# Integrate alternatives into plan
return self._integrate_alternatives(plan, failure["step"], alternatives)# Research Agent
class ResearchAgent(Agent):
"""Agent specialized for research tasks."""
default_tools = ["web_search", "document_reader", "summarizer"]
default_capabilities = ["information_gathering", "synthesis", "citation"]
def __init__(self, name: str, config: dict = None):
super().__init__(
name=name,
role="Research Specialist",
tools=self.default_tools,
config=config
)
def research(self, topic: str, depth: str = "comprehensive") -> dict:
"""Conduct research on a topic."""
return self.execute_task({
"type": "research",
"topic": topic,
"depth": depth,
"deliverables": ["summary", "sources", "key_findings"]
})
# Coding Agent
class CodingAgent(Agent):
"""Agent specialized for coding tasks."""
default_tools = ["code_generator", "code_reviewer", "test_runner", "debugger"]
def __init__(self, name: str, languages: list = None, config: dict = None):
super().__init__(
name=name,
role="Software Developer",
tools=self.default_tools,
config=config
)
self.languages = languages or ["python", "javascript"]
def write_code(self, specification: str, language: str = "python") -> dict:
"""Write code based on specification."""
return self.execute_task({
"type": "code_generation",
"specification": specification,
"language": language,
"include_tests": True
})
# Analysis Agent
class AnalysisAgent(Agent):
"""Agent specialized for data analysis."""
default_tools = ["data_loader", "statistical_analyzer", "visualizer", "report_generator"]
def analyze(self, data_source: str, analysis_type: str) -> dict:
"""Analyze data and generate insights."""
return self.execute_task({
"type": "data_analysis",
"data_source": data_source,
"analysis_type": analysis_type,
"output_format": "report"
})class CompositeAgent(Agent):
"""Agent that can delegate to specialized sub-agents."""
def __init__(self, name: str, sub_agents: list):
super().__init__(name=name, role="Coordinator")
self.sub_agents = {agent.name: agent for agent in sub_agents}
def execute_task(self, task: dict) -> dict:
"""Execute task, delegating to appropriate sub-agent."""
# Determine which sub-agent should handle the task
assigned_agent = self._assign_agent(task)
if assigned_agent:
# Delegate to sub-agent
result = assigned_agent.execute_task(task)
return self._integrate_result(result, task)
else:
# Handle directly if no suitable sub-agent
return super().execute_task(task)
def _assign_agent(self, task: dict) -> Agent:
"""Assign task to most suitable sub-agent."""
task_type = task.get("type", "")
# Match task type to agent capabilities
for agent in self.sub_agents.values():
if task_type in agent.capabilities:
return agent
return None
# Usage
research_agent = ResearchAgent("researcher")
coding_agent = CodingAgent("developer")
analysis_agent = AnalysisAgent("analyst")
coordinator = CompositeAgent(
name="project_lead",
sub_agents=[research_agent, coding_agent, analysis_agent]
)agent_config = {
"name": "ProductionAgent",
"role": "General Assistant",
# Reasoning configuration
"reasoning": {
"strategy": "chain_of_thought",
"temperature": 0.3,
"max_reasoning_steps": 10,
"confidence_threshold": 0.7
},
# Planning configuration
"planning": {
"max_plan_depth": 5,
"parallel_execution": True,
"adaptive_replanning": True
},
# Execution configuration
"execution": {
"max_retries": 3,
"timeout_seconds": 300,
"checkpoint_frequency": "per_step"
},
# Memory configuration
"memory": {
"type": "vector",
"capacity": 10000,
"persistence": True
},
# LLM configuration
"llm": {
"provider": "openai",
"model": "gpt-4",
"max_tokens": 4096
}
}
agent = Agent.from_config(agent_config)In this chapter, you've learned:
- Core Components: Reasoning, planning, execution, and memory
- Reasoning Engine: Chain of thought, ReAct, and tree of thought
- Decision Making: Scoring actions and confidence thresholds
- Planning Module: Goal decomposition and adaptive planning
- Agent Types: Task-specific and composite agents
- Configuration: Setting up agent behavior
- Modular Architecture: Components work together but are independently configurable
- Reasoning Strategies: Different strategies for different problem types
- Adaptive Planning: Plans evolve based on execution results
- Specialization: Task-specific agents for better performance
- Composition: Combine agents for complex workflows
Now that you understand agent architecture, let's explore Tool Integration in Chapter 3 to connect agents with external capabilities.
Ready for Chapter 3? Tool Integration
Generated for Awesome Code Docs
Most teams struggle here because the hard part is not writing more code, but deciding clear boundaries for self, dict, reasoning so behavior stays predictable as complexity grows.
In practical terms, this chapter helps you avoid three common failures:
- coupling core logic too tightly to one implementation path
- missing the handoff boundaries between setup, execution, and validation
- shipping changes without clear rollback or observability strategy
After working through this chapter, you should be able to reason about Chapter 2: Agent Architecture as an operating subsystem inside SuperAGI Tutorial: Production-Ready Autonomous AI Agents, with explicit contracts for inputs, state transitions, and outputs.
Use the implementation notes around context, config, agent as your checklist when adapting these patterns to your own repository.
Under the hood, Chapter 2: Agent Architecture usually follows a repeatable control path:
- Context bootstrap: initialize runtime config and prerequisites for
self. - Input normalization: shape incoming data so
dictreceives stable contracts. - Core execution: run the main logic branch and propagate intermediate state through
reasoning. - Policy and safety checks: enforce limits, auth scopes, and failure boundaries.
- Output composition: return canonical result payloads for downstream consumers.
- Operational telemetry: emit logs/metrics needed for debugging and performance tuning.
When debugging, walk this sequence in order and confirm each stage has explicit success/failure conditions.
Use the following upstream sources to verify implementation details while reading this chapter:
- View Repo
Why it matters: authoritative reference on
View Repo(github.com). - AI Codebase Knowledge Builder
Why it matters: authoritative reference on
AI Codebase Knowledge Builder(github.com).
Suggested trace strategy:
- search upstream code for
selfanddictto map concrete implementation paths - compare docs claims against actual runtime/config code before reusing patterns in production