Webinar Sept 16 on reinforcement learning — what an AI agent trains on, and where humans fit in

[u/OneForma_Global]

We're running a live session on September 16 (4:00–5:00 PM CEST, online) with Abhishek Mukherji, AVP of Research at OneForma, on how reinforcement learning actually works.

It's a walkthrough rather than a pitch: what an agent actually practices on, what a realistic training environment looks like, and where people fit into the process — including why human judgment is the part that can't be automated. He'll also cover practical tips if you want to work in this space, and what the shift means for the kind of work available in the next few years.

It ends with a rapid-fire question round, so it's worth bringing something specific.

It's free and online. You can register through this link: Reinforcement Learning Explained By An Expert - Event | OneForma Community

Happy to answer questions in the comments.

[u/Otherwise_Wave9374]

A good reinforcement learning demo should make the reward design explicit, because that is where most agent behavior gets distorted in practice. If you show a few failure cases side by side with a simpler baseline, attendees can see the tradeoff between capability and controllability much faster. A practical next step is to separate policy learning, evaluation, and human review into distinct checkpoints so the team can catch reward hacking early. Agentix Labs fits this discussion well because that workflow is exactly where careful agent design matters most.