The Future of GRC: How AI Agents and Orchestration Will Shape the Next Wave

Introduction

Over the past decade, governance, risk, and compliance (GRC) has steadily evolved from a collection of siloed programs into an enterprise-wide strategic function. Yet as risks multiply, regulations increase, and the interconnectedness between cyber, third-party, and operational risks becomes ever more complex, the GRC function needs the agility and efficiency that AI can provide.

For risk and compliance professionals, this isn’t merely a technological upgrade; it’s a shift in how effectively they partner with AI agents to structure workflows, elevate decision-making, and build greater resilience.

In this blog, we explore how the convergence of AI agents and orchestration mechanisms is transforming GRC, outline what it means for practitioners, and how you can stay prepared.

AI-First GRC: The Moment is Now

Several converging factors are accelerating the need for AI-first GRC, especially agentic AI.

• The risk landscape is more connected, fast-moving, and cross-domain than ever. Traditional periodic risk assessments are ineffective in keeping up with cyber vulnerabilities, vendor risks, and evolving business models.
• Regulatory change is relentless. According to Thomson Reuters, the financial services sector alone experienced an average of 257 regulatory changes a day. This is even before factoring in major new acts like the EU AI Act, US SEC Cybersecurity Rules, and DORA.
• The demand for resilience, not just compliance. Organizations are shifting from “are we compliant?” to “are we resilient, agile, and able to respond to risk dynamically?”

When regulatory velocity and risk complexity are both accelerating, an AI-first GRC strategy powered by agentic and generative AI empowers professionals to shift from reactive checklist compliance to proactive risk mitigation and strategic decision making. In fact, according to our 2025 GRC Practitioner Survey, 43 % of organizations are actively evaluating AI solutions for GRC.

The Orchestration of AI Agents and the Role of MCP

A powerful way to visualize the next wave of GRC is through the metaphor of an orchestra. As Gaurav Kapoor, Co-Founder and Vice Chairman, MetricStream, explains in his article for OCEG:

“I like to think of the human/AI interaction as a symphony — working together in harmony to drive forward powerful productivity, simplify processes, and amplify outcomes. But AI is not the conductor. AI is the orchestra. The true conductor remains the risk leader: setting the score, keeping tempo, and ensuring harmony between human judgment and intelligent agents.”

There are the AI agents, which are specialized, purpose-built computational entities that handle defined risk/compliance tasks (e.g., vulnerability detection, third-party due diligence, regulatory change scanning, contract risk extraction, control testing). Each agent is like an orchestral section (violins, brass, percussion) with its own part to play.

Then there is the orchestration layer, which is the coordination mechanism that ensures agents work in concert rather than in silos, managing context, sequencing, dependencies, hand-offs, and governance.

This is where Model Context Protocol (MCP) steps in. MCP is an emerging framework that helps manage and orchestrate multi-agent AI systems. It serves as the communication and coordination layer between AI agents, data sources, and human oversight. MCP provides 3 critical capabilities:

1. Contextual awareness: It ensures that AI agents don’t operate in silos. For example, if a compliance agent detects a policy breach in a business unit, the operational risk agent can automatically adjust the risk score for that unit and trigger mitigation workflows.
2. Task routing & sequencing: MCP acts as an orchestration layer that decides which agent acts when, in what order, avoiding duplication or conflict.
3. Governance & guardrails: MCP embeds rules, escalation paths, human-in-the loop checkpoints, audit logs, and ethical standards. This ensures AI agents act responsibly and are aligned with regulatory expectations and organizational controls.

MCP operates within a broader ecosystem of orchestration and governance frameworks that enable AI to work securely and responsibly. Policy-as-code tools like Open Policy Agent define the rules and compliance requirements that guide agent decisions. Interoperability standards such as APIs, JSON schemas, and function-calling ensure agents can communicate and share data effectively.

Meanwhile, AI risk management frameworks like the NIST AI RMF provide the guardrails for ethical, transparent, and auditable AI use. Together, these elements empower MCP to act as the orchestration layer, keeping every agent aligned with enterprise policies and regulatory expectations.

Why MCP and AI Orchestration Matter for GRC Professionals?

AI agents and orchestration fundamentally change how risk, compliance, and assurance are managed, helping the GRC function become more intelligent, connected, and continuous. The impact goes beyond efficiency. It reshapes roles, decision-making, and how organizations build trust and resilience. Your risk and compliance team gain the following benefits.

• Move from reactive to proactive: Agents can scan, model, alert, and route rather than waiting for the quarterly review.
• Scale faster: Rather than each risk domain building its own tool, an orchestrated multi-agent approach allows reuse, shared context and unified workflows.
• Enable humans to focus on judgement and strategy: With AI doing the heavy lifting of data-ingestion, pattern-recognition and monitoring, you as the risk or compliance leader, can focus on interpreting insights, driving actions and engaging the board.
• Build a unified, connected risk view: Orchestrated agents break down domain silos (cyber, third-party, operational) and help create an enterprise-wide risk narrative.

How Risk and Compliance Teams Can Prepare

The shift to AI-agent orchestration in GRC requires careful preparation. Here are the 5 key steps for practitioners:

1. Define outcomes and the orchestration model

   Before deploying agents, outline the end goals you want to achieve, whether it's faster incident-to-mitigation workflows, real-time vendor risk scoring, or end-to end audit coverage. Set clear objectives, scope, and success metrics, such as cycle-time reduction, fewer risk events, regulatory compliance, or cost savings.

2. Build or leverage purpose-built agents and map workflows

   Identify high-value tasks such as regulatory monitoring, control testing, contract review, vendor onboarding, and cyber monitoring. Deploy agents designed for these tasks and define their sequence: who acts first, what data is passed along, and where human review is needed. For example: Cyber-threat agent Vendor exposure agent Compliance-reporting agent Board alert.

3. Ensure data, integration, and context readiness

   Orchestrated multi-agent systems rely on shared context, common data models, unified taxonomy, and high-quality data. Integration across the silos of risk, compliance, audit, third-party, and cyber is foundational.

4. Embed governance, ethics, and human-in-loop oversight

   AI agents introduce new risks, including model bias, hidden decision logic, and accountability gaps. The orchestration layer must define which decisions require human review, how outputs are validated, and how audit trails are maintained. Human oversight ensures responsible, compliant AI adoption.

5. Cultivate culture, skills, and change readiness

   Orchestrated AI in GRC is as much about people as technology. Teams need data literacy, collaboration across the first, second, and third lines of defense, and an agile mindset. Building trust in AI-augmented workflows and upskilling employees is essential for long-term success.

How MetricStream is Enabling this Future with our AI-First Strategy

Risk and compliance professionals stand at a pivotal crossroads. The future demands a new orchestration of intelligence, where AI agents execute specialized tasks, an orchestration layer ensures they sync and flow, and you, as the conductor, guide, interpret, and orchestrate.

At MetricStream, we are committed to this future. Our AI-first Connected GRC products are built to simplify your GRC work, amplify your outcomes, enable orchestration across domains, and give you the clarity, speed, and resilience that modern risk and compliance management demands. We are integrating purpose-built AI capabilities and agents across the GRC lifecycle, including:

• AI insights and recommendations for risk & control rationalization, control test prioritization, and clustering of issues & actions
• AI-infused workflows to identify duplicate issues, recommend actions, perform policy search, and rank third-party risks
• Agentic and generative AI to auto-populate data, create risks, controls, issues, and actions, assess and score risks, and capture evidence across multiple systems
• AI GRC administration agents (Admin, Support, QA, and Upgrade) to streamline platform operations.

Together, these innovations make GRC simpler, faster, and smarter – and will make your AI and risk management strategy sing. Want to learn more? Request a demo now.