Ai Agent Platform

VoltAgent AI Agent Platform

What Are AI Agents?

AI agents wrap Large Language Models (LLMs) with tools, memory, and reasoning capabilities. Unlike chatbots that respond to single prompts, agents:

• Plan multi-step problem solutions
• Call APIs, databases, and external services via tools
• Persist context across conversations and sessions
• Coordinate with other agents
• Evaluate and adapt through feedback loops

Why Building AI Agents Takes Time

Shipping agents means solving:

• Orchestration: Coordinating agents, tools, and workflows
• Memory: Persisting conversation history and semantic context
• Retrieval: Grounding responses in proprietary data (RAG)
• Safety: Input validation, output filtering, guardrails
• Observability: Trace visualization, cost tracking, debugging
• Deployment: Scaling across edge, serverless, and server environments
• Monitoring: Performance, quality, and cost analytics

Building this from scratch requires significant engineering effort.

What You Need to Build AI Agents

Building production AI agents requires solving 11 categories of infrastructure:

Core Runtime

The foundation of any agent system. You need a runtime that manages agent lifecycle, coordinates LLM calls, and handles responses safely without reinventing the wheel for every project.

• Agent orchestration engine
• LLM provider abstraction (OpenAI, Anthropic, Google, etc.)
• Streaming response handling
• Cancellation & timeout controls
• Type-safe agent definitions

📖 [Agent Over