This article is the first in a two-part series. The second article can be found here: How open Agent Protocols (MCP, MCP Proxy Bridge, ACP, A2A, A2H/H2A) future-proof your stack.
Listen to the audiogram that complements this article for a more immersive understanding.
AI agents have matured. They no longer just handle tickets, lookup FAQs, book meetings, or handle singular tasks—they can now handle complex business processes, and in the world of CX, drive onboarding, adoption, renewals, expansion, and full-lifecycle success at scale.
AI agents did not leap from FAQ chatbots to full-fledged operators overnight. Instead, they have matured through three stages—single-task, role-based, and process-owning—each adding coordination and business impact.
We’ve all been there—stuck repeating ourselves as we’re passed from one support rep to another.
Customers don’t actually mind being transferred to an expert. What frustrates them is having to re-explain the issue every single time.
That’s why they crave a single, knowledgeable helper—not because one person (or agent) can do it all, but because context is rarely preserved.
So if a single superhuman can’t solve every problem, how do we coordinate multiple intelligent agents—without creating chaos?
Should we rely on an all-knowing Super Agent that handles everything?
Or orchestrate a team of Specialist Agents that work in sync?
There’s no one-size-fits-all answer. Both approaches have real benefits—and real trade-offs. This post unpacks the difference and where the future is headed.
The coordination problem exists with AI agents too. A “super agent” tries to do everything in one brain. A team of specialist agents, however, needs clear protocols to pass context—just like great human teams do.
OpenAI Operator — “Buy groceries, update LinkedIn, book travel.” A single agent handles diverse tasks through one conversational interface.
xAI’s Grok — Designed to be an all-in-one interface for knowledge, planning, and execution across Tesla, X, SpaceX, and beyond.
Rewind.ai — Captures your full digital life—emails, docs, calls, meetings—and lets one agent answer questions across it all.
Inflection AI’s Pi — A generalist AI agent optimized for empathetic conversation, guidance, and broad-scope reasoning. Sold to Microsoft.
Rabbit R1 — A device-first generalist agent that operates apps on your behalf via “Large Action Model” (LAM); designed for task execution through a unified interface.
Humane Pin — Sold to HPE
OpenAI io Products — likely Super Agent.
Anthropic Claude Research — Investigating collaborative multi-agent systems that reason and act together.
Microsoft AutoGen — A multi-agent orchestration framework where planners, coders, testers, and critics coordinate to solve complex tasks.
ReAct — A prompting framework where a single model alternates between reasoning and taking tool-based actions, simulating agent collaboration.
DeepMind’s Gemini Agents — Distributed agentic subsystems for perception, reasoning, and memory, working in tandem to solve tasks. Masked by a singular interface.
LangChain Agents — Enables modular agents to route tasks across tools and APIs, forming the foundation for modern RAG pipelines.
BabyAGI — Early proof-of-concept where an agent autonomously creates and manages task-specific sub-agents on the fly.
Skip the extremes between generalist-only or specialist-only agentic models.
Most enterprises win with a hybrid—one (or a few) powerful generalists supported by focused domain specialists, all wired together through open protocols.
Think “AI CSM” steering the customer lifecycle while Feedback and Support agents handle surveys and Tier-1 FAQs automatically. Everything hands off cleanly because the agents share a common language.
Here's a practical framework:
One expert agent per business discipline, connected by open protocols.