Orientation: Build Enterprise-Grade AI into Your Product

What is Aegis?

Aegis helps engineering teams move from proof-of-concept LLM prototypes to robust, production-ready AI systems. It provides modular microservices for building, running, evaluating, and scaling intelligent agents — all while staying fully under your control.

From internal automation to AI-powered product features, Aegis offers the flexibility to start small and grow into a complete platform.

Who is Aegis for?

Aegis is built for engineering teams who want to:

Quickly prototype intelligent agents with tools and structured outputs
Expose those agents behind secure API endpoints
Gradually adopt enterprise-grade features like access control, retrieval, orchestration, and evaluation
Maintain full visibility and control over infrastructure, logic, and costs

Aegis is not a no-code chatbot builder — it’s a stack for engineering-led AI integration into real-world products.

Two Parts, One Stack

Agent Core

Prototype fast. Deploy instantly.

Register agents with tools, prompts, and structured outputs
Hit secure API endpoints to run agents in minutes
Extend with tool chaining, output schemas, and batch execution

Enterprise Modules

Add production-grade infrastructure when you’re ready.

Retrieval-Augmented Generation (RAG)
Graph-based agent orchestration
Structured evaluation pipelines
Authenticated, rate-limited gateway
Cost tracking, usage limits, and API key management

Why Aegis Is Different

Aegis isn’t just a toolkit — it’s a set of opinionated design choices built for real-world AI operations:

Modular Microservices Deploy only what you need. Scale independently.
Config-Driven Agents Agents, tools, and teams are serialized as config — not locked in code. This enables versioning, GitOps deployment, and declarative orchestration. → Learn more about structured agents and config-driven teams
Evaluation-Native Score, trace, and monitor agent activity with built-in feedback loops. → Learn more about evaluation and observability
Structured Semantic RAG for Agents Retrieve from a structured, access-controlled document graph using semantic links and chunk-level filters. → Learn more about structured semantic RAG
Not a Black Box You control the prompts, execution, and outputs — with validation built in.
Multi-Tenant from Day One Visibility, access, rate limits, and budgets per tenant/user/team.
Observability Built In Logs, traces, metrics, and replay tooling included out of the box.

Who is this documentation for?

There are three types of users who come to this site — follow the path that fits your role:

1. CTOs and Technical Decision Makers

Want to understand the architecture, risks, and tradeoffs in bringing AI into their product stack.

➡️ Start with the Agent Handbook

2. Engineers Prototyping AI Features

Need to register an agent and call it behind an API quickly — then iterate with tools and outputs.

➡️ Start with the Agent Quickstart

3. Engineers Preparing for Production

Have validated their use case and want to bring up the full stack: access control, RAG, orchestration, evaluation, and more.

➡️ Start with the Production Deployment Guide

For a high-level overview of the platform components and how they fit together, see the Platform Overview.