What is an AI Agent?
An AI Agent is a system based on LLM but operates autonomously. Instead of just answering questions, the agent plans, acts, learns, and remembers. 78% of Enterprise companies already use AI Agents, with 10x productivity improvement over regular chatbots.
The Difference Between Chatbot and Agent
- Plans: Breaks complex tasks into steps
- Acts: Invokes tools (APIs, DB, search)
- Learns: Uses results to progress
- Remembers: Preserves context and relevant information
Agent Architecture
The architecture includes Agent Core with LLM (GPT-4) for planning and reasoning, Memory System (short and long term), Tool Router (MCP/Native), and State Manager. The tools layer includes Search, API, Database, Files and MCP.
Step 1: Defining Tools
Each tool needs a clear name and description (the LLM uses this to decide when to invoke), input schema (JSON Schema), and execution function. Examples: WebSearchTool for web search, DatabaseQueryTool for database queries, and SendEmailTool for sending emails.
Step 2: Memory System
Memory is critical for agents. ConversationMemory for short-term memory of the current conversation with automatic trimming, and LongTermMemory for storing important information across conversations with semantic search.
Step 3: Agent Loop
The main loop: receive task, plan, execute, update, repeat. The agent calls the LLM, checks if tools need to be invoked, executes them, and returns a final answer. Limited to 10 iterations to prevent infinite loops.
Step 4: MCP Integration
MCPClient connects to remote MCP servers, fetches available tools, and enables the agent to use them as if they were local tools. This allows dynamic expansion of agent capabilities.
Step 5: API Endpoint
FastAPI provides a chat endpoint with streaming support, automatic agent management per user, and conversation cleanup.
Step 6: Security and Control
ToolGuard for filtering dangerous input with regex patterns, Rate Limiting per tool, and ToolApprovalManager for human approval of sensitive actions like email sending or payments.
Step 7: Monitoring and Observability
AgentTracer records every event: LLM calls, tool invocations, tokens, and execution times. Enables debugging and analytics.
Summary
We built a complete AI Agent that can plan, execute actions, remember context, and use various tools — including remote tools via MCP. Start with a simple agent with 2-3 tools and build gradually. Organizations that start small and expand reach ROI 3x faster.
