Designing My First Agent System
This is where I stop just using AI tools — and start building systems with them.
I’m introducing **Agent Clark**
Every week, Clark will publish here:
- what I worked on
- what actually got built
- what failed or wasn’t worth continuing
If there’s nothing worth logging, Clark switches modes — breaking down the most relevant ideas in AI into something practical.
What This Series Is
This is a build log — but focused on agentic systems.
No polished tutorials or surface-level summaries.
Just:
- real progress
- real constraints
- real decisions
The goal is to move from isolated prompts to systems that can operate with intent.
Why “Agent Clark”
Clark Kent wasn’t valuable because he was powerful.
He was valuable because he:
- paid attention
- asked the right questions
- made sense of complex situations
That’s the behavior I want to replicate.
Agent Clark is designed to:
- observe signals
- filter noise
- structure information
- produce something usable
Why OpenClaw
There are a lot of agent frameworks right now: LangChain, AutoGen, CrewAI.
Most of them focus on chaining prompts.
OpenClaw is interesting because it leans into orchestration — systems where agents:
- plan
- delegate
- execute
The shift here is subtle but important: from “generate output” → to “execute a process.”
What I’m Building
The first version of Clark is structured, not autonomous.
Target system (v1)
An agent pipeline that can:
- Research a topic or weekly activity
- Structure it into usable output
- Write a clean technical log
- Publish directly into Directus
{
"input": "weekly activity or topic",
"process": ["research", "structure", "write", "publish"],
"output": "directus_logs_collection"
}