AI Skills & How They Work
AI Orchestration Frameworks
Orchestration frameworks chain models, tools, and data into multi-step workflows and agents. They save boilerplate and impose structure, but they also add abstraction, debugging cost, and lock-in. The right choice is usually the smallest framework that fits your workflow, and often no framework at all.
Punti chiave
- Start with plain code: a loop calling an API with retries is often enough; add a framework when repetition hurts.
- Frameworks differ in abstraction level: some manage prompts and chains, others manage agents, tools, and state.
- State and memory are the hard part: conversation history, tool results, and user context must be managed explicitly.
- Observability is non-negotiable: log prompts, tool calls, latencies, and costs, or you cannot debug failures.
- Tool calling needs guardrails: validate arguments, limit permissions, and require confirmation for destructive actions.
- Agents compound errors: each step can fail, and failures cascade; add checkpoints and human approval.
- Lock-in is real: framework-specific abstractions make migration expensive; keep business logic out of framework code.
- Evaluate the whole pipeline: end-to-end success rate matters more than any single component's score.
Domande frequenti
Do I need a framework to build an AI app?
No. Many production systems use direct API calls; frameworks help when workflows get complex.
LangChain or LlamaIndex?
LangChain for chains and agents; LlamaIndex for data ingestion and retrieval; both overlap, so pick by your primary pain.
Are agents production-ready?
For narrow, well-scoped tasks with guardrails, yes; for open-ended autonomy, not yet.
How do I debug an agent?
Trace every step, tool call, and model output; without traces you are guessing.
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