Moving from simple LLM-powered chatbots to fully autonomous agents is one of the steepest challenges in modern AI development. Most developers struggle with maintaining context, managing secure tool execution, and ensuring that agents don’t perform destructive actions in production environments.
The Ceiling of Traditional LLM Implementation
Early attempts at agentic workflows relied heavily on raw API calls. While this approach works for basic question-answering or simple scripted tasks, it lacks the agency required for complex, multi-step reasoning. Unlike standard static LLM implementations, real agents need a persistent environment to interact with the world safely.
Introducing Flue: The Agent Harness Framework
The Flue framework solves this by providing a programmable TypeScript harness designed for autonomy. It isn’t just another SDK; it is the infrastructure required to give models the context, tools, and security they need to function as independent workers.
| Capability | Standard API Call | Flue Agent |
|---|---|---|
| Context Persistence | Ephemeral/Stateless | Durable Sessions |
| Tool Access | Manual/Scripted | Typed Tools & MCP |
| Security | None (Host relies on dev) | Secure Sandboxes |
| Complexity | Low-level loops | Structured Workflows |
Key Features for Agentic Engineering
Flue enables you to build agents that can safely take action, maintain continuity, and connect to the systems where work already happens. The framework provides several critical layers for production-grade deployment:
- Durable Execution: Learn how agents preserve progress through failures and restarts.
- Observability: Monitor your agent’s reasoning via advanced observability integration with OpenTelemetry or Sentry.
- MCP Connectivity: Connect your agents to authenticated services using the Model Context Protocol (MCP) ecosystem.
- Subagents: Define specialized roles and let your primary agent delegate work to the right expert.
Implementing a Programmable Harness
The power of Flue lies in its TypeScript-first approach. You can define an agent’s entire universe—from the model choice to the file system permissions—in a single, readable configuration. By following the guide on building agents, you can orchestrate these components into complex, automated structured workflows.
// agents/triage.ts
import { defineAgent, type AgentRouteHandler } from '@flue/runtime';
import { local } from '@flue/runtime/node';
import triage from '../skills/triage/SKILL.md' with { type: 'skill' };
export default defineAgent(() => ({
model: 'anthropic/claude-sonnet-4-6',
tools: [...githubTools],
skills: [triage],
sandbox: local(),
instructions: 'Triage a bug report end-to-end...',
}));Conclusion
Whether you are running agents locally via CLI or deploying them to a hosted runtime, Flue provides the security and continuity needed for agentic engineering. It allows your agents to use tools, modify files, and complete real work within a secure sandbox.
Ready to start building? Explore the Flue repository on GitHub today and deploy your first autonomous agent.
