Living in a Multi-Agent + Human World: Inside Rezolve.ai's Agentic Studio

What to Expect in This Session

• What AI agents, MCP, and A2A protocols actually mean — in plain language

• The four building blocks of Rezolve.ai's Agentic Studio: AI Functions, Agents, Workflows, and Agentic Apps

• How MCP integrations work, and why Rezolve.ai's toggle-on approach matters for governance

• Full observability across every layer of the stack

• Where humans stay in the loop — approval flows and agent-to-human collaboration

INTRODUCTION

This breakout session from Rezolve Connect 2026 took attendees inside Agentic Studio — Rezolve.ai's platform for building, deploying, and governing AI agents. Joshua O'Brien, Head of Generative AI at Rezolve.ai, guided the session with live product demonstrations, covering everything from the foundational concepts of agentic AI to the human collaboration layer that keeps autonomous systems accountable.

The session was designed for both technical and non-technical audiences. If you've been hearing terms like 'AI agent,' 'MCP,' or 'A2A' and aren't sure how they connect to real IT service outcomes, this is where it comes together.

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First, Let's Speak the Same Language

Before showing the platform, the session established shared definitions for three terms that are reshaping enterprise IT — and are often misunderstood.

WHAT IS AN AI AGENT?

A standard chatbot takes a prompt and returns a response. An AI agent does something more: it reasons about the input, decides what actions to take, accesses tools to gather information or execute tasks, and works iteratively toward a real objective. The distinction matters because it changes what the technology can actually do for your organization.

"An AI agent is probably the closest thing you're going to get to an employee. It can take action, reason through problems, and actually accomplish real tasks for you."  — Joshua O'Brien, Head of Generative AI, Rezolve.ai

A simple way to think about it: an agent is an AI with a job description and a toolbox. For a deeper grounding in how agents work, see: What Are AI Agents? A Complete Guide to Understanding Intelligent Agents

WHAT IS MCP?

MCP stands for Model Context Protocol. Like TCP and other foundational tech protocols, its purpose is standardization — specifically, a uniform way for AI agents to discover and use tools.

Before MCP, connecting an agent to an external system required writing or configuring a custom API integration for every tool. MCP changes that. A compatible tool advertises its capabilities, required inputs, and expected outputs. An agent queries the MCP server, gets a structured list of what it can do, and uses it — no custom code required.

"I've heard MCP described as USB-C for AI. You point your agent at an MCP-compatible server and it just works."  — Joshua O'Brien, Head of Generative AI, Rezolve.ai

Further reading on this topic: From APIs to MCP: Why Model Context Protocol Is the Future of Agentic AI Integrations

WHAT IS A2A (AGENT-TO-AGENT PROTOCOL)?

A2A is what happens when one agent is not enough. Complex enterprise tasks often require multiple areas of expertise — web research, ticket creation, HR queries, access provisioning. A2A enables specialized agents to operate independently and communicate through a shared standard.

An orchestration agent acts as the single entry point. It receives the request, determines which specialized agents to engage, passes context between them, and synthesizes the result — all through a standardized protocol, without requiring custom code for each handoff.

"Each agent operates independently in its own area of expertise, but they communicate through structured protocols. The orchestration layer handles the context switching between them."  — Joshua O'Brien, Head of Generative AI, Rezolve.ai

Example scenario: an employee reports an issue through the help desk bot. Before creating a ticket, the orchestration agent checks whether a web search can surface a self-service fix. If it can't, it passes to the ticketing agent. The employee sees one seamless conversation — multiple agents collaborated behind the scenes.

Related: One Billion AI Agents by 2026: What This Means for ITSM

The Four Building Blocks of Agentic Studio

Agentic Studio is organized around four core components, each serving a distinct purpose in the architecture.

1. AI Functions — Input/Output Operations

AI functions are the simplest building block: pure input/output operations. They accept defined inputs, perform a focused AI task, and return a structured output. No reasoning loop, no tool access — just transformation or classification.

An example is the built-in ticket classifier function. It accepts a support ticket text and a list of valid categories, then returns the predicted category, subcategory, reasoning, and confidence score. Functions are composable — they can serve as tools inside agents or as steps inside workflows.

2. Agents — Autonomous Reasoning

Agents are where autonomous reasoning begins. An agent in Agentic Studio can consume AI functions, call MCP endpoints, and access external APIs. It reasons about what to do next, selects tools, and iterates toward a result.

Building one is fast. The AI-assisted agent builder generates a starting configuration from a plain-language description. A debug panel shows exactly what the agent is thinking at each step — its plan, tool calls, outputs, and reflections. After any test run, teams can save it as a test case, rate it, describe what should have happened, and use that feedback to let the AI refine the agent automatically.

Agents deploy via public URL, API, Microsoft Teams, or Slack — configured directly in the studio.

3. Workflows — Deterministic Processes

Agents are powerful but autonomous — they don't always follow a prescribed sequence. When you need a defined process (step A, then step B, then a conditional check), that is what workflows are for.

"Workflows are your tool when you want something deterministic. When you're interacting with production systems and need to trace exactly what happened — for reliability, compliance, or auditability — that's where workflows belong."  — Joshua O'Brien, Head of Generative AI, Rezolve.ai

A workflow can include agent nodes, AI function nodes, webhook integrations, and human approval steps. The AI workflow builder generates a starting structure from a natural language description, including nodes, connections, and suggestions for what to configure next.

Related reading: Enterprise Workflow Automation: Why, When, What, and How

4. Agentic Apps — The Unified Entry Point

Agentic apps sit at the top of the stack. They serve as the interface layer for end users and the orchestration layer for everything underneath. A single agentic app can expose multiple agents, multiple workflows, and multiple AI functions through one unified experience.

An employee support app, for example, might include an IT support agent, an HR support agent, a user onboarding workflow, and an offboarding workflow — all accessible from a single interface. This is where A2A operates in practice: the app orchestrates across agents and workflows, handling context switching transparently.

MCP in Practice: Granular Control and Full Auditability

Adding an MCP server in Agentic Studio requires three things: a name, an endpoint URL, and authorization credentials. Once connected, the platform discovers all available tools automatically.

What differentiates Rezolve.ai's implementation is the default-off approach. When a new MCP server is added, all tools are disabled. Administrators explicitly enable only the tools an agent should use.

WHY THIS MATTERS

First, context management: giving an agent access to too many tools degrades performance and can cause unpredictable behavior. Second, security: when MCP first emerged, agents were performing actions that their implementers never intended, because no one had reviewed the full scope of what the MCP server exposed.

"In our implementation, everything is off by default. You go through and tell it exactly what tools you want it to use. When you need to explain your AI governance posture to compliance, you can show exactly what's enabled and what it's doing."  — Joshua O'Brien, Head of Generative AI, Rezolve.ai

Teams can also override the default names and descriptions that MCP servers provide. This matters more than it sounds — an agent uses tool descriptions to decide when to invoke them. A poorly worded default description can cause an agent to never use a critical tool, or to use it inappropriately. Custom descriptions give teams precise behavioral control at the individual tool level.

Every MCP tool has its own analytics dashboard: total calls, success rate, average latency, most-used tools, and a full log of every input and output. All exportable for compliance reporting.

For deeper context on AI governance and observability requirements: AI Governance in ITSM: New Compliance Rules for 2026

Observability Across the Entire Stack

Visibility exists at every level. AI functions, agents, workflows, and MCP endpoints each have analytics pages covering execution counts, success rates, latency, call sources, and full input/output logs.

At the highest level, the Agentic Studio dashboard provides an executive view: workflow executions, agentic app runs, status distribution (successful, failed, pending approval), and success rates by workflow. Agent interaction logs show every conversation an agent had. Workflow run logs provide step-by-step traces of every execution.

This observability is not just for troubleshooting. It is the foundation for communicating AI governance: exactly what ran, exactly what it did, and exactly what it produced.

Human Collaboration: The Built-In Check

Full autonomy was never the goal. Rezolve.ai's architecture is designed for genuine human-AI collaboration — agents handling what they can handle, humans stepping in where judgment, approval, or clarification is genuinely needed.

The Human Collaboration board is where that handoff happens. When an agent reaches a decision point it cannot resolve on its own, it surfaces a request to a human. When a workflow includes an approval step, that approval lands here. Managers review context, approve or reject, and the workflow continues.

"We think the AI agent should be able to reach out to a human and say, 'I'm stuck here — I don't know what to do. Can you help?' That collaboration capability, not just approval flows but genuine agent-to-human communication, is something we're really excited about."  — Joshua O'Brien, Head of Generative AI, Rezolve.ai

This makes Agentic Studio distinct from platforms where AI operates in a black box. Every exception, every approval, every clarification request is visible, traceable, and reviewable.

See how this connects to enterprise agentic AI adoption: Agentic AI for Enterprises: AI-Driven Organizational Intelligence

The Multi-Agent + Human World, In Practice

The four building blocks represent a design philosophy: compose the right level of intelligence and determinism for each task, connect through open standards, and keep humans in the loop where it counts. MCP makes integrations plug-and-play. A2A makes multi-agent coordination structured and auditable. The Human Collaboration board ensures autonomy never comes at the cost of oversight.

That is the architecture of the multi-agent, multi-human world — and it is running in production today.

Frequently Asked Questions

What is the difference between an AI agent and a chatbot?

A chatbot responds to inputs. An AI agent reasons about inputs, plans, uses tools, and takes actions to accomplish a goal. The distinction matters because agents can complete multi-step tasks autonomously, while chatbots are limited to single-turn responses.

What does MCP stand for, and why does it matter for enterprise AI?

MCP stands for Model Context Protocol. It is a standardized way for AI agents to discover and use external tools — similar to how USB-C standardizes device connections. For enterprises, it means AI integrations become plug-and-play rather than requiring custom coding for each new tool.

Why does Rezolve.ai default MCP tools to 'off'?

Security and context management. Enabling all tools in an MCP server can expose agents to capabilities they should not have and create compliance risks. Defaulting to off and requiring explicit enablement ensures that every tool an agent can access has been deliberately reviewed and approved.

When should I use a workflow versus an agent?

Use an agent when you want autonomous reasoning and flexible decision-making. Use a workflow when you need a deterministic, auditable sequence — particularly when interacting with production systems or when compliance requires a clear record of exactly what happened.

How does human collaboration work in Agentic Studio?

The Human Collaboration board centralizes all approval requests, clarification requests from agents, and escalations. When an agent is stuck or when a workflow requires an approval, the relevant context surfaces here for a human reviewer to act on.

Where can I watch the full session?

Watch on YouTube  or access all sessions at Rezolve Connect 2026 On Demand

Explore Rezolve.ai's Agentic AI platform  →