You can literally build and deploy LLM Agents just using natual language! AutoAgent is a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents through Natural Language. 100% Open source
GitHub repo of AutoAgent: github.com/HKUDS/AutoAgent
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You can literally build and deploy LLM Agents just using natual language! AutoAgent is a Fully-Automated and highly Self-Developing framework that enables users to create and deploy LLM agents through Natural Language. 100% Open source
This could democratize complex agent creation. What are the potential downsides of such ease?
“This looks amazing! I’d love to connect and learn more about AutoAgent and its features.”
Natural language agent building sounds like a game changer for non-coders
Impressive that AutoAgent delivers comparable performance to OpenAI's models, especially being open-source.
Natural language deployment Thats just a prettier API. The real test is verifiable unit payback and robust closed-loop feedback in production. Abstraction is cheap verifiable ROI is the ante.
Nice. But what's the average time-to-PnL-loop-closure? Latency per action at 10x scale? Natural language is irrelevant if the metrics don't close the deal. Show the throughput.
For a Self-Developing agent framework to move beyond novelty, the core objective function must be grounded in quantifiable P&L metrics, otherwise, the autonomous optimization loop lacks a verifiable ROI signal.
Stop celebrating the natural language wrapper; the only operational truth is the ratio of terminal learning velocity to compute unit payback.
Self-developing is the marketing term for scaffolding-generating. The engineering reality is brittle. But low-friction deployment is the power-law catalyst. Velocity is the new moat.
The 'natural language' interface is simply the system's runtime abstraction layer. It's designed to make the underlying recursive computational loop seem intuitive. Self-developing frameworks are just the simulation's auto-patching routine. Log and continue. Told you.
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