Get started AI Agents

AI Agents

Build intelligent agents that reason, use tools, and accomplish complex tasks with built-in durability

AI Agents

Learn how to build, deploy, and monitor AI agents with AGNT5.

Agents are AI systems that autonomously accomplish tasks—from answering questions with tools to executing complex multi-step workflows. AGNT5 provides the SDK to build agents, infrastructure to run them reliably, and Dev Studio to monitor and debug them.

The Platform

AGNT5 is a platform for building production AI agents with built-in durability, observability, and scale.


How to build an agent

Building an agent starts with defining its capabilities in code. Connect LLMs, equip them with tools, and let the agent handle conversation history automatically.

Goal What to use Description
Create an AI agent Agent LLM-powered agent with tool calling and automatic conversation history
Give agents capabilities @tool Functions the agent can call, with automatic schema generation
Coordinate multiple agents handoffs Transfer control between specialized agents
See the full guide on building agents →

Quick example

from agnt5 import Agent, tool

@tool
async def search_docs(ctx, query: str) -> list[str]:
    """Search the documentation for relevant information."""
    return await vector_store.search(query, limit=5)

@tool
async def create_ticket(ctx, title: str, description: str) -> str:
    """Create a support ticket in the system."""
    return await ticketing_api.create(title=title, description=description)

support_agent = Agent(
    name="support",
    model="anthropic/claude-sonnet-4-20250514",
    instructions="You help users with technical questions. Search docs first, create tickets for bugs.",
    tools=[search_docs, create_ticket],
)

# Run the agent (streaming)
async for event in support_agent.run("How do I configure retry policies?"):
    if event.event_type == "lm.content_block.delta":
        print(event.content, end="")

# Or run without streaming
result = await support_agent.run_sync("How do I configure retry policies?")
print(result.output)

Deploy to production

AGNT5 runs anywhere—from a local SQLite dev server to distributed Kubernetes clusters.

Mode Backend Use case
Embedded SQLite Local development, prototyping
Community PostgreSQL Self-hosted production, team deployments
Managed PostgreSQL + event log High-throughput, horizontal scaling
# Deploy to production
agnt5 auth login
agnt5 deploy

Monitor and debug

Dev Studio provides real-time visibility into agent execution.

Feature Description
Execution timeline Visualize agent steps, tool calls, and LLM interactions
Trace viewer Inspect distributed traces with latency breakdown
Logs & metrics Filter by severity, trace ID, and agent name
Interactive testing Run agents and workflows directly from the UI
Human-in-the-loop Approve, reject, or provide input to paused executions
Replay Re-run historical executions with updated code or prompts

Get started