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I designed a Notion AI agent that helps turn messy client requests into a structured and manageable workflow. Instead of leaving important details scattered across messages, the agent organizes each request inside Notion, decides whether it should become a new entry or update an existing one, and creates a linked analysis page with the key details.
The analysis can include the request summary, deadline, risks, clarifying questions, next steps, and version history. When new information arrives, the agent keeps it as an update instead of overwriting the previous analysis, so the context is not lost over time.
I also made the system flexible, so users can adapt it to their own workflow - remove unnecessary sections, add missing ones, or keep the setup as simple as they need.
I intentionally built this agent in Ukrainian, even though the language is not officially supported, because the platform is quite popular in Ukraine and the competition rules allow this format.
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I ran a session with my team on how to build a team of AI agents, and how to treat them so they actually deliver.
What we've seen running this setup on client work:
→ Agents running 24/7
→ 3B+ tokens in 6 days
→ A 2-week milestone done in 3 days
The 5 things I taught the team:
Treat agents like teammates, not software.
Give them a clear goal, the right context and tools they're comfortable with.
Tell them the goal, never the solution.
Let the agents propose the options. You make the call.
Talk to one agent, not ten.
A central PM agent takes the goal and coordinates the rest.
Solve today's problem.
Don't build the perfect agent on day one. Start small and let the team grow with the work.
Don't be the bottleneck.
Review at milestones, not every task.
For clients, this changes the pace. My job is understanding the problem and making the decisions. The agents handle the execution.
Highlights from the session 🎥
Building something and want to move faster with an AI-first team? Let's talk.
A strong specialist skill is useless if the wrong system gets invoked. Long-running AI work breaks in boring, expensive ways.
That is not a prompting problem. It is a routing and authority problem.
How I can help you:
I build project routers for teams using multiple AI workflows, agents, or specialist assistants so that they work with each other as a team.
I design source-of-truth resolutions so your agents retrieve the current rules instead of improvising or relying on assumptions or stale chat data.
I create reusable skill registries and specialist handoff logic hardened by determined and dedicated flow states reducing the need for model reliance on its own internal and displaced memory context.
I separate project states with individualized canonical states bound by rules living under essential model instructions and routed properly so unrelated workflows do not contaminate one another, but exist with awareness of the role each project plays, enabling a persistently growing ecosystem to develop organically.
I add governance wrappers, escalation hooks, and recovery mirrors around existing AI stacks so that trust is designed at the root, and variance is eliminated with rule based source archiving and implementation.
Trust means closing your eyes at night and sleeping soundly because your agents got your back. And I can put it all together for you, precision results sourced and undisputed because I got tired of doing things the hard way.
Phillip, agree that routing is where it breaks. In our builds the worst bugs came from an agent pulling an old rule from chat history instead of the current source. Pinning one source of truth per project fixed more than any prompt change did.
Onboarding workflow diagram for Resplendent Data, a B2B business intelligence platform
Check it out ⭣