AI agents and workflows
4 totalAgent platforms, workflow orchestration, knowledge-base Q&A and automation.
Chat is temporary; workflows are repetitive. Products in this category turn "ask once" into "run every day": connect documents into knowledge-base Q&A, send model output into spreadsheets and systems, and chain steps into branching workflows.
The deciding factor is not model capability but where it connects: can it read your documents, call your existing systems, and show every intermediate step when something fails?
Choose by the action you need to repeat:
- A pile of documents to query: use knowledge-base Q&A. Focus on adjustable chunking and retrieval, not the model name.
- Cross-system processes to automate: use workflow orchestration. Check the number of ready connectors, branching and retry support.
- Publishing for others: choose a platform that provides share links or an app form, so people can use it without code.
- Data cannot leave: choose an open-source framework you can deploy locally and run it with your own model.
- Not sure you need a workflow yet: document the manual process in a chat assistant first, then decide whether to move it into a workflow.
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Frequently asked questions
How is an agent different from ordinary chat?
An agent has fixed instructions, accessible documents and tool calls, and the same setup can be reused or shared. A chat is a one-off context. Writing your repeated requests down as a setup is the simplest form of an agent.
Why is knowledge-base Q&A inaccurate?
Most failures are retrieval problems, not model problems: chunks are too long, tables are split into noise, or one topic is scattered across files. Look at the cited snippets first; if they locate the source text, the answer can usually be fixed.
Is local deployment complicated?
An open-source framework started with Docker can run on a GPU machine in about half an hour. The hard part comes later: model selection, vector-store maintenance, upgrades and backups. If you lack maintenance capacity, a hosted online platform is easier.
What happens when an automated workflow fails?
Keep workflows short and put human confirmation at key points. Choose a product that persists intermediate results so you can tell whether the model answered wrong or the data never arrived.
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