Orchestration Patterns for Building AI Agents at the Edge

Source: Dev.to

What Are Agents, Really?

An agent is a system designed to autonomously pursue a goal. It is more than just an LLM. Think of an agent as having three key components:

• The Brain (LLM) – the core reasoning engine
• The Senses (Inputs) – perception of the world
• The Hands (Tools) – ability to act on the world

An LLM alone is just a “brain in a jar.” An agent must interact with its environment.

Why a Single Agent Fails in Production

Movies often portray a single omniscient AI (think Skynet). In reality, current LLMs are powerful but brittle—they’re like “children with encyclopedias,” easily distracted and prone to failure on multi‑step tasks. Relying on one agent leads to:

• Context overload – no inherent memory, causing higher token usage and cost
• Brittle execution – a single failure forces a full restart
• Information leakage – a single point of access can expose sensitive data

The Generalist Intern Problem

A lone agent resembles an eager but overwhelmed intern. When overloaded, it experiences:

1. Context Overload – too many tools and inputs increase latency and cost.
2. Complete Breakage – a failure at any step aborts the whole workflow.
3. Information Leakage – sensitive data can be unintentionally disclosed.

The Security Problem

Consider an agent that automatically replies to emails:

1. It reads a legitimate bank statement and summarizes it.
2. An attacker sends a phishing email asking for the account ID and balance.

Because the agent has access to the confidential information, it may inadvertently disclose it. When a single agent has unrestricted access, the risk of data leakage skyrockets.

Agents Must Live on the Edge

Agents should run close to users—in edge servers that deliver millisecond‑level latency. A typical agentic flow looks like:

STT → LLM → Tool Call → LLM → TTS

Every millisecond adds up, degrading user experience. Cloudflare Workers, Durable Objects, Workers AI, AI Gateway, and the Agents SDK address this by providing:

• Ephemeral Workers for stateless computation
• Durable Objects for persistent state and coordination

If you’re unfamiliar with Durable Objects, see this excellent explainer by Boris Tane.

Common Agent Patterns: The Solution

Instead of a single monolith, organize agents into a team with two primary types:

Ephemeral Agents

• Execute a single task in isolation
• Destroyed immediately after completion
• No memory of past interactions
• Ideal for security‑sensitive operations

Permanent Agents

• Long‑running identity
• Maintain persistent state
• Coordinate workflows and aggregate results
• Handle routing and orchestration

Core Patterns

• Router – a permanent agent that directs requests to the appropriate worker.
• Worker – an ephemeral agent that performs a single action.
• Fleet Manager – spawns and monitors workers, handling scaling and health.

These building blocks can be combined to solve complex problems, such as the email‑reply scenario described earlier, while preserving security, reliability, and low latency.