110 lines
3.7 KiB
Markdown
110 lines
3.7 KiB
Markdown
# Integrating AI Agents Into Scheduled Workflows
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## Forward
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AI, especially AI agents, are undeniably powerful today. Integrating
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agents into scheduled tasks is actually a very natural next step as it
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unlocks NLP in such workflows.
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With the recent on-going development of `wp-materialize` and using it
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to sync a blogs repo to my [personal
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website](https://peisongxiao.com), and some tasks given during my
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co-op, it's becoming one of the things on the top of my head.
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---
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## Necessity of agents
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If we trace this topic using first principles, the biggest question
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isn't "How to integrate agents into workflows?", it's:
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> When to use agents?
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Quite frankly, this is a nuanced question. You can practically do
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anything with agents that you would normally do on your device. But is
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that really what you need for cronjobs?
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*We don't need agents for everything.*
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### In applications
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The simplest example out there: alarms.
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It would be hilarious if someone scheduled an agent task for every
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single alarm (but there are potential ways agents could be
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integrated).
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An application with a well-defined purpose doesn't need AI.
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### Classic pipelines
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For example, a well-established task doesn't need agents. If your
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cronjob involves a simple, deterministic script with verbose output
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would simply involve a message-sending channel and a log dump, perhaps
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a parser in front to decide if a dump is actually needed.
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This is largely applicable to existing scheduled jobs, no need to
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spend money and time in setting up agents when the task itself is
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**well-defined**.
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## So, when do we need agents?
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The gist of it is simply:
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> When the task isn't well-defined and needs changing behavior.
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When the task involves arbitrary input that requires human
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understanding or reasoning, an agent could be a better fit.
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The end-goal of having any scheduled task solidified into either a
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deterministic script or application logic or an agent task, is to
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minimize human intervention when doing repeated jobs. And agents do
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quite well as a substitute for human reasoning and actions.
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### Example: Scheduled Policy Review
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Policies, unless in a machine-readable format, is not friendly to any
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script. LLMs are one of the best parsers for such input, agents may
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even discover new relevant policies as they arise.
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If the task is to review the policy and suggest actions, then an agent
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is definitely a good choice.
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---
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## But how?
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### Accessibility
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After using [OpenClaw](https://openclaw.ai/), I found that setting up
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scheduled agents has never been easier for simple workflows: you
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simply talk to OpenClaw about your needs, and it will set things up
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for you.
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But that's just one wrapper around such logic, more tools will
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definitely come. And there's nothing stopping anyone from setting up
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their own framework or a one-shot job if they have the knowledge to do
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so.
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But for someone that doesn't have access to such knowledge and tools,
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it's up to someone to package that into a consumer-friendly UI/UX
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that's also safe to use. We're not going to dive into the depths of
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what agents can and cannot do in this post.
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### What to do
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Without diving into the depths, there's only one thing I can say about
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this:
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> Be explicit.
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Being explicit about the context, the goals, and the
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boundaries. They're what makes any agent powerful and safe.
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---
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## Reflections
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Agents are just another tool in the task pipelines. Treat them as
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the key to unlocking new possibilities, not as something you must use
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for every job.
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Consumer availability is still limited, but that landscape is rapidly
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changing.
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> Agents don’t replace pipelines. They sit at the boundary where
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> pipelines fail. That boundary is where human judgment used to live,
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> and where agents now belong.
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