Raghav Sehgal

Well written breakdown @musamolla . The key insight is spot on: generative AI creates, but agentic AI decides and executes. What we’re seeing with enterprises is that value now comes from stitching both layers together. GenAI interprets intent, drafts options, and synthesizes knowledge; agentic AI turns that into multi-step action with memory, tools, and feedback loops. The real shift isn’t autonomy alone but reliable orchestration—letting AI handle outcomes, not just outputs. At a21.ai, this hybrid pattern is already powering workflow automation and decision systems across finance, pharma, and ops. The convergence is where the true leap happens.

you've captured the essence of GenAI in finance @ghostai1 — and totally agree that the real frontier isn’t just automation, but new decisioning and revenue models. In our work with banks at a21.ai, we’re seeing GenAI accelerate risk scoring, fraud detection, underwriting, portfolio insights, and customer personalization. The biggest shift is AI moving from “assistive” to agentic, handling multi-step workflows end to end. Finance industry won’t just use GenAI to analyze data but it will move (some already moving) to agentic system to act autonomously on data, but of course with guradrails, controls and auditability. Exciting times ahead !

Love this breakdown @bharatcoder — especially the part about patterns being the real story, not the frameworks. What we’re seeing in enterprise builds is that these four patterns (sequential, hierarchical, parallel, iterative) show up everywhere, regardless of whether you use OpenAI Agents, ACP, A2A, or your own orchestrator.

One thing I’d add: the real unlock comes when these patterns mix.
A router agent might delegate tasks hierarchically, spawn parallel workers, then run an iterative improvement loop on the final result. That’s when systems start feeling genuinely autonomous instead of “fancy workflows.”

Regards,
Raghav
a21.ai