by zh2408 on 3/19/25, 9:29 PM with 80 comments
by DebtDeflation on 3/20/25, 11:00 AM
https://www.anthropic.com/engineering/building-effective-age...
"- Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
- Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks."
What Anthropic calls a "workflow" in the above definition is what most of the big enterprise software companies (Salesforce, ServiceNow, Workday, SAP, etc.) are building and calling AI Agents.
What Anthropic calls an "agent" in the above definition is what AI Researchers mean by the term. It's also something that mainly exists in their labs. Real world examples are fairly primitive right now, mainly stuff like Deep Research. That will change over time, but right now the hype far exceeds the reality.
by campbel on 3/19/25, 10:55 PM
Agentic systems can be simply the LLM + prompting + tools[1]. LLMs are more than capable (especially chain-of thought models) to breakdown problems into steps, analyze necessary tools to use and then executing the steps in sequence. All of this is done with the model in the driver seat.
I think the system described in the post need a different name. It's a traditional workflow system with an agent operating on individual tasks. Its more rigid in that the workflow is setup ahead of time. Typical agentic systems are largely undefined or defined via prompting. For some use cases this rigidity is a feature.
[1 https://docs.anthropic.com/en/docs/build-with-claude/tool-us...
by zh2408 on 3/19/25, 9:29 PM
OpenAI Agents: for the workflow logic: https://github.com/openai/openai-agents-python/blob/48ff99bb...
Pydantic Agents: organizes steps in a graph: https://github.com/pydantic/pydantic-ai/blob/4c0f384a0626299...
Langchain: demonstrates the loop structure: https://github.com/langchain-ai/langchain/blob/4d1d726e61ed5...
If all the hype has been confusing, this guide shows how they actually work under the hood, with simple examples. Check it out!
https://zacharyhuang.substack.com/p/llm-agent-internal-as-a-...
by _pdp_ on 3/19/25, 11:07 PM
Forget about boxes and deterministic control and start thinking of error tolerance and recovery. That is what agents are all about.
by mentalgear on 3/19/25, 10:34 PM
by jumploops on 3/20/25, 1:44 AM
tl;dr from Anthropic:
> Workflows are systems where LLMs and tools are orchestrated through predefined code paths.
> Agents, on the other hand, are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks.
Most “agents” today fall into the workflow category.
The foundation model makers are pushing their new models to be better at the second, “pure” agent, approach.
In practice, I’m not sure how effective the “pure” approach will work for most LLM-assisted tasks.
I liken it to a fresh intern who shows up with amnesia every day.
Even if you tell them what they did yesterday, they’re still liable to take a different path for today’s work.
My hunch is that we’ll see an evolution of this terminology, and agents of the future will still have some “guiderails” (note: not necessarily _guard_rails), that makes their behavior more predictable over long horizons.
[0]https://www.anthropic.com/engineering/building-effective-age...
by miguelinho on 3/19/25, 11:48 PM
by nxpnsv on 3/20/25, 6:43 AM
by admiralrohan on 3/20/25, 9:36 AM
by bckr on 3/20/25, 2:40 AM
by v3ss0n on 3/20/25, 7:25 AM
by DrFalkyn on 3/20/25, 4:04 AM