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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.

Kľúčové body

  1. Start with plain code: a loop calling an API with retries is often enough; add a framework when repetition hurts.
  2. Frameworks differ in abstraction level: some manage prompts and chains, others manage agents, tools, and state.
  3. State and memory are the hard part: conversation history, tool results, and user context must be managed explicitly.
  4. Observability is non-negotiable: log prompts, tool calls, latencies, and costs, or you cannot debug failures.
  5. Tool calling needs guardrails: validate arguments, limit permissions, and require confirmation for destructive actions.
  6. Agents compound errors: each step can fail, and failures cascade; add checkpoints and human approval.
  7. Lock-in is real: framework-specific abstractions make migration expensive; keep business logic out of framework code.
  8. Evaluate the whole pipeline: end-to-end success rate matters more than any single component's score.

Často kladené otázky

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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