AI Agent System
Autonomous agents that can reason, use tools, and execute multi-step tasks with full observability.
What is an Agent?
An agent is an AI-powered execution unit that receives a goal, reasons about how to achieve it, selects and uses appropriate tools, and produces structured outputs. Unlike simple prompt chains, agents have agency—they decide which actions to take based on context and feedback.
Reasoning Engine
Agents use LLMs to plan, make decisions, and adapt to unexpected situations.
Tool Selection
Automatically chooses the right tool for each sub-task from a registry of capabilities.
Memory & Context
Maintains conversation history and retrieves relevant past interactions.
Structured Output
Returns validated JSON responses that can be consumed by downstream steps.
Agent Configuration
Agents are defined with a system prompt, model selection, tool access list, and execution constraints. Each agent runs in isolation with its own context window.
{
"name": "research_assistant",
"system_prompt": "You are a research assistant. Your job is to gather information from the web, analyze it, and provide concise summaries.",
"model": "gpt-4",
"temperature": 0.7,
"max_tokens": 2000,
"tools": ["web_search", "text_summarizer", "file_writer"],
"memory_enabled": true,
"max_iterations": 5
}Agent Execution Flow
Receive Goal
Agent gets a high-level task description.
Plan Actions
LLM breaks down the goal into executable steps.
Execute Tools
Agent calls tools sequentially or in parallel.
Return Result
Final output is validated and logged.
Agent Safety & Guardrails
- • Maximum iteration limits prevent infinite loops
- • Tool permissions are enforced at the executor level
- • Output validation ensures structured responses
- • All actions are logged for audit and debugging