I’m trying to move beyond simple AI agents — what makes an agentic system actually useful?

AI Discussions
1

I have been experimenting with multi-agent systems and recently started building a DeepResearch-style agent workflow to understand what goes into a real Agentic AI application.

The first version is intentionally simple, but the goal is not just another “LLM + tool call” demo. I’m trying to learn the engineering patterns behind reliable agents.

Current workflow:

  • Planner → breaks a complex goal into tasks

  • Research agents → gather information and evidence

  • Reflection → identify missing information or weak results

  • Synthesis → combine findings into a report

  • Critique/refinement → improve the final output

While building this, I realized the hard parts are not just calling an LLM. The interesting challenges seem to be:

  • How should agents communicate?

  • When should an agent decide to use tools?

  • How do you evaluate whether an agent is actually improving?

  • What is the right memory/state architecture?

  • When do multiple agents help vs just add complexity?

I’m currently improving this project and would love feedback from people building agent systems:

  • What architecture patterns have worked well for you?

  • What features separate a demo agent from a useful agent?

  • What mistakes should I avoid while scaling this?

I’m sharing the code in the comments for anyone interested in reviewing or experimenting with it.

Would appreciate any thoughts from this community.

GitHub:

I’m especially interested in feedback on:

* architecture decisions

* agent orchestration patterns

* evaluation methods

* ideas for making this closer to a production-style agent system

Open to suggestions and contributions.

2

I think the real challenge is not adding more agents, but making sure the results are actually good. More agents can help with bigger tasks, but they can also make the whole setup harder to manage.

I’d probably spend more time on testing and checking where the information comes from. That makes it much easier to figure out when something goes wrong.

The planner, research, reflection and synthesis flow looks pretty solid though. I’ve been following agentic AI projects through AI Tool Hunt as well, so I’ll be interested to see how this develops.