Agent Architectures
From Chatbots to Autonomous Agents
AI agents represent a paradigm shift from passive question-answering systems to autonomous entities that can perceive, reason, plan, and act. Modern agent architectures combine LLMs with tools, memory, and reasoning capabilities to solve complex tasks.
Core Components of an Agent
- Perception: Understanding the environment and user intent
- Memory: Short-term working memory and long-term knowledge
- Reasoning: Planning, decision-making, and problem decomposition
- Action: Tool use, API calls, and environment interaction
- Learning: Improving from feedback and experience
Modern Agent Architecture
A typical agent architecture with perception, memory, planning, and action modules orchestrated by an LLM core. Hover over components to see details.
Information Flow:
- • Perception → LLM: Environmental input
- • Memory ↔ LLM: Context retrieval/storage
- • LLM → Planning: Goal decomposition
- • LLM → Action: Tool invocation
Feedback Loops:
- • Action → Perception: Environment changes
- • Planning → Memory: Store successful plans
- • All modules → LLM: Status updates
- • LLM → All: Coordination signals
Agent Design Patterns
1. ReAct (Reasoning + Acting)
Interleaves reasoning traces with actions. The agent thinks step-by-step about what to do next, then takes an action, observes the result, and reasons again.
2. Plan-and-Execute
First creates a complete plan, then executes it step by step. More efficient but less adaptive to unexpected results.
3. Reflexion
Adds self-reflection after task completion. The agent critiques its own performance and learns from mistakes.
4. Tree of Thoughts
Explores multiple reasoning paths in parallel, evaluating and pruning branches to find optimal solutions.
ReAct Agent Execution
Watch how a ReAct agent interleaves reasoning (thoughts) with actions to solve a complex task step by step.
ReAct Pattern Benefits:
- • Transparent reasoning process
- • Adaptive to unexpected results
- • Easy to debug and improve
- • Natural integration with tools
Memory Systems
Working Memory
- Current task context
- Recent observations
- Intermediate results
- Active plans
Long-term Memory
- Past experiences
- Learned procedures
- Domain knowledge
- User preferences
Retrieval Mechanisms
- Semantic similarity search
- Temporal relevance
- Importance scoring
- Forgetting curves
Agent Memory System
See how different types of memories are stored and retrieved based on relevance to the current query. Memories fade over time unless reinforced.
Working Memory
Current context and recent interactions. High relevance, fast decay.
Episodic Memory
Past experiences and events. Retrieved by similarity.
Semantic Memory
Facts and knowledge. Slow decay, high reusability.
Tool Integration
Common Tool Categories
- Information Retrieval: Search, databases, APIs
- Computation: Code execution, calculators, simulators
- Communication: Email, messaging, notifications
- Creation: File generation, image creation, data visualization
- Environment Control: IoT devices, system commands
Tool Selection Strategies
- Semantic matching of tool descriptions
- Learning from demonstration
- Trial and error with feedback
- Hierarchical tool composition
Challenges and Solutions
Hallucination and Grounding
Agents must ground their actions in reality. Solutions include tool verification, consistency checking, and human oversight.
Error Recovery
Robust agents need to handle tool failures, invalid outputs, and unexpected states through retry logic, alternative paths, and graceful degradation.
Safety and Alignment
Preventing harmful actions requires careful prompt engineering, action filtering, and constitutional AI approaches.
Why Agent Architectures Matter
- Automation: Handle complex multi-step tasks autonomously
- Adaptability: Respond to changing environments and requirements
- Scalability: Solve problems beyond single model capabilities
- Personalization: Learn and adapt to individual users
- Integration: Bridge AI capabilities with real-world systems