An AI agent is an autonomous software entity that perceives its environment, makes decisions, and takes actions to achieve specific goals. Unlike traditional chatbots or simple AI models, agents can plan, reason, use tools, and adapt their behavior based on feedback.
Agents operate independently, making decisions without constant human intervention. They can plan sequences of actions and adapt strategies based on outcomes.
Every agent has specific objectives to achieve. They break down complex goals into manageable tasks and work systematically towards completion.
Agents perceive and interact with their environment, whether it's accessing databases, calling APIs, or processing user inputs.
Modern agents can learn from interactions, improve their strategies, and adapt to new situations using feedback loops and memory systems.
Input Processing
Decision Making
Strategy Formation
Task Execution
Feedback Integration
The core reasoning engine, typically an LLM like GPT-4, Claude, or LLaMA. Handles understanding, generation, and decision-making.
External capabilities the agent can invoke: APIs, databases, calculators, code execution, web browsing, etc.
Short-term (conversation context) and long-term (vector databases) memory for maintaining state and learning.
Structured instructions that define agent behavior, personality, constraints, and decision-making patterns.
Manages agent lifecycle, tool calling, error handling, and coordination between multiple agents.
Monitors agent performance, collects metrics, and provides feedback for improvement.
Agents break down complex problems into step-by-step reasoning chains, making their thought process explicit and verifiable.
User: "Plan a trip to Tokyo for 5 days"
Agent Thought Process:
1. First, I need to understand the travel dates and budget
2. Research main attractions in Tokyo
3. Organize attractions by location to minimize travel
4. Create day-by-day itinerary
5. Add restaurant recommendations near each location
6. Include transportation tips between locations
7. Suggest accommodation areas based on itinerary
Output: [Detailed 5-day Tokyo itinerary]
Combines reasoning with action-taking in an interleaved manner. The agent thinks, acts, observes results, and adjusts.
Thought: I need to find the current weather in Tokyo
Action: weather_api.get_weather("Tokyo")
Observation: Temperature: 22°C, Clear skies
Thought: Good weather for outdoor activities. Let me check cherry blossom status
Action: seasonal_api.get_sakura_forecast("Tokyo")
Observation: Peak bloom expected in 3 days
Thought: Perfect timing! I'll prioritize outdoor gardens and parks
Action: create_itinerary(focus="outdoor", special="sakura viewing")
Explores multiple reasoning paths simultaneously, evaluating different approaches before committing to the best one.
# Multi-Agent Collaboration Example
Research Agent: "I'll gather information about Tokyo attractions"
Planning Agent: "I'll organize the attractions into an efficient route"
Budget Agent: "I'll calculate costs and find deals"
Content Agent: "I'll write engaging descriptions for each location"
Orchestrator: "Coordinating all agents to produce final itinerary..."
| Capability | Basic Agent | Advanced Agent | Multi-Agent System |
|---|---|---|---|
| Task Decomposition | Simple linear tasks | Complex hierarchical tasks | Distributed parallel tasks |
| Tool Usage | 1-2 basic tools | Multiple specialized tools | Tool sharing across agents |
| Memory | Session-based | Persistent with retrieval | Shared knowledge base |
| Error Handling | Basic retry logic | Adaptive strategies | Fault tolerance & redundancy |
| Learning | None | In-context learning | Collective intelligence |
# Simple Python Agent Example
class SimpleAgent:
def __init__(self, llm, tools=None):
self.llm = llm
self.tools = tools or {}
self.memory = []
def think(self, input_text):
# Add input to memory
self.memory.append({"role": "user", "content": input_text})
# Generate response with context
response = self.llm.generate(
messages=self.memory,
system="You are a helpful assistant that can use tools."
)
# Check if tool use is needed
if self.should_use_tool(response):
tool_result = self.execute_tool(response)
return self.think_with_observation(tool_result)
return response
def execute_tool(self, tool_call):
tool_name = tool_call.get("tool")
if tool_name in self.tools:
return self.tools[tool_name].execute(tool_call.get("params"))
return "Tool not found"