The Hypothesis

Can we package a complete, functional AI agent into a single Docker container that runs anywhere with zero configuration?

The Concept

Deploying AI agents currently requires stitching together model APIs, vector databases, orchestration layers, tool registries, and monitoring. This experiment asks: can we package all of that into a single Docker container? Pull, run, and you have an agent.

Agent in a box

The hypothesis

Can we package a complete, functional AI agent into a single Docker container that runs anywhere with zero configuration?

The concept

Deploying AI agents currently requires stitching together model APIs, vector databases, orchestration layers, tool registries, and monitoring. This experiment asks: can we package all of that into a single Docker container? Pull, run, and you have an agent.

How it works

1. Pull container — docker pull agentlabs/agent:latest
2. Container bundle — agent runtime, tool registry, vector store, memory layer, API gateway, health checks
3. Deploy — docker run — zero configuration
4. Agent is live — accepts tasks via REST API on :8080

Everything an agent needs to function, packaged into a single deployable unit.

What it explores

• Can a useful agent fit in a single container without external dependencies?
• What’s the minimum viable agent architecture that’s still production-grade?
• How do you handle model access — bundled small model vs. external API?
• Can container-based agents coordinate with each other in a mesh?
• What’s the performance penalty of an all-in-one package vs. distributed components?

What we found

• A useful agent container lands at ~2GB with a small bundled model
• Zero-config is achievable for simple task agents but complex agents need environment configuration
• Single-container model works well for development and staging, less so for production scale
• Container-to-container agent communication works with a shared network

Learnings

• The line between “simple enough for zero-config” and “needs configuration” is blurrier than expected — this boundary needs explicit design, not assumption.

• Single-container packaging is a dev/staging/edge pattern, not a production one — production needs independent component scaling.

• Agent coordination doesn’t require complex infrastructure — a shared network and simple REST contracts are enough to start.

Where it goes next

This experiment directly informs AgentOS’s deployment model. The idea of self-contained, portable agents is central to how we think about the runtime layer — especially for edge deployment and air-gapped environments.