The Olympic Games showcases extraordinary athletic performances. However, medals are rarely won on talent alone. Behind the winning athelete lies an intricate system of preparation, coordination, governance, and trust. As organizations enter the Agentic AI era, the same is becoming true in IT service management (ITSM).
Success will depend not only on deploying capable AI agents, but also on creating the conditions that empower them to reach their full potential. From coordinating multiple AI agents to ensuring humans and AI agents work well together, the Olympics offers a useful lens for exploring what ITSM can learn.
The race is won at the baton exchange
Relay teams know that races aren’t won by speed alone; they’re won in the handoff. A clean baton exchange preserves momentum; a poor one erases it. Swap the relay team for a stack of AI agents in ITSM, and the lesson holds. As organizations deploy specialized AI agents across service workflows, success hinges on how effectively context flows between them.
Consider onboarding a temporary finance contractor who will work remotely from abroad. An onboarding AI agent coordinates specialized AI agents for identity provisioning, endpoint setup, software licensing, and access governance. However, when each AI agent’s memory is transient and scoped to the task it receives, it results in a loss of key business context.
The contractor’s temporary status and short engagement – the one detail that should have shaped the access – never make the journey across the workflow. With no shared context layer to fall back on, the access-governance AI agent can’t recover what it was never told. Hence, it resorts to its predefined behavior. Each AI agent completes its job, yet permanent access is granted where time-bound privileges were intended to apply.
Organizations that excel in the agentic era will treat context as the baton. The contractor’s engagement status is now built into every scoped task or stored in a shared context layer that each AI agent can read. Every AI agent understands not only the task it must perform but also the business intent behind it.
For example, the access governance AI agent grants time-bound privileges, the licensing AI agent matches entitlements to the engagement period, and the endpoint AI agent provisions for remote work. The result is more than efficient automation: it is coordinated, faster, and more compliant actions, with context flowing as seamlessly as the work itself.
Test out of competition, not just on race day
Olympic teams don’t wait until competition day to assess performance. They continuously evaluate training, fitness, and preparation throughout the season. The winning principle is simple – success confidence comes from ongoing validation, not a one-time assessment.
For example, an AI agent that passed UAT six months ago should not be assumed to remain effective, reliable, or trustworthy today. As your AI agents gain access to enterprise systems and take on greater responsibility, validation must evolve from a one-time milestone into an ongoing discipline.
A security-based Agentic AI example
Consider a resolution AI agent that:
- Resets passwords
- Restarts services
- Retrieves account information.
Before launch, it is tested against realistic service requests and performs flawlessly, but that testing assumes ticket descriptions are genuine.
Months later, a security-weakness attacker submits a ticket with carefully crafted instructions designed to influence how the AI agent reads the request. Because the AI agent considers ticket content in its decision-making, it accepts the attacker-controlled text as legitimate and performs a privileged action.
Your resolution AI agent isn’t malfunctioning. It’s still operating exactly as designed. However, it was simply never tested against adversarial input, nor reevaluated as new security-attack techniques emerged.
Teams that stay ahead will treat validation as continuous – regularly challenging AI agents with new attack patterns, edge cases, and simulated misuse. The goal is not simply to identify failures, but to ensure AI agents remain reliable as conditions, threats, and operating environments evolve.
Every Olympic team needs a playbook
Olympic teams don’t win by assembling the best athletes and specialists alone. Success comes from every athlete, coach, and support member understanding their role, decision rights, and responsibilities. Without this, even the most talented roster pulls in different directions rather than toward a shared goal. An AI workforce is no different. As your organization deploys more specialized AI agents across service management, its success depends not just on what each AI agent can do, but on how clearly the agent’s role is defined in the broader operating model.
Consider an event-management AI agent that automatically remediates infrastructure issues. This might work alongside an incident-management AI agent that diagnoses and resolves incidents triggered by those same events. Individually, both perform well.
Issues arise when their responsibilities overlap. During service degradation, the event-management AI agent may restart a service while the incident-management AI agent deliberately holds it in a controlled state to collect diagnostic evidence. Each action is reasonable in isolation, yet together they undermine the resolution effort.
The opposite issue appears when ownership is unclear. For example, a request-fulfillment AI agent provides a new application, and an access-governance AI agent grants entitlements. But neither of these agents is responsible for obtaining approval from the manager within their scope, missing a crucial step.
The best operators will manage their AI workforce much as Olympic teams manage their specialists: with clearly defined responsibilities, escalation paths, and decision boundaries. After all, a collection of specialists without defined ownership isn’t a team; it’s a collision waiting to happen.
The rule book has to keep pace with the gear
The controversy over polyurethane swimsuits and today’s super-shoes was never really about technology. It was about capability arriving faster than the rule book. Sporting bodies had to answer a hard question: not whether athletes could use these innovations, but whether they should. Agentic AI in ITSM faces a similar challenge. As AI agents become more capable, governance must evolve alongside what they’re allowed to do.
Consider a major incident-management AI agent. First, its role is operational – correlating alerts, opening incidents, coordinating responders, and recommending remediation actions.
Over time, your organization connects the AI agent to additional sources of context, including service maps, business impact data and regulatory policies. Its capabilities have expanded; it can now:
- Assess customer impact
- Estimate revenue loss
- Weigh regulatory obligations
- Model alternative recovery paths.
During a major outage, the AI agent identifies several response options
- Restoring service quickly, but creating temporary compliance exposure
- Minimizing regulatory risk but extending downtime.
Historically, business leaders weighed these trade-offs because they involve organizational priorities, risk tolerance, and accountability, not just technical considerations.
The challenge with AI agents
The challenge here isn’t that the AI agent lacks capability. It may analyze the options faster and more thoroughly than any human team. The challenge, instead, is determining where technical autonomy ends and business judgment begins.
The most mature organizations will establish governance that defines:
- Which decisions AI agents can make
- Which decisions require human judgment
- Who remains accountable for the decisions.
Capability may advance rapidly, but governance must keep pace.
No Olympic champion skips the fundamentals, nor does successful Agentic AI
Every Olympic performance rests on something unglamorous, including:
- Conditioning
- Reliable equipment
- Accurate timing
- Trustworthy data on the athlete’s form.
Skip the fundamentals, and no amount of strategy makes up for it.
Likewise, in ITSM, the most sophisticated AI agents are only as good as their foundation:
- Accurate, current configuration and service data they can trust
- The right access to tools across the stack
- Observability that keeps every decision traceable and auditable
- Well-defined integrations so context can flow between systems.
The road to the podium (including for Agentic AI)
As AI agents multiply and decisions become less visible, ITSM must evolve from managing tickets and tasks to orchestrating intelligence:
- Seamless context handoffs
- Continuous validation
- Clear ownership
- Governance that keeps pace with capability.
Done well, this enables organizations to deploy more AI agents with confidence rather than chaos. Ultimately, ITSM excellence in the agentic era will be measured not by how many AI agents are deployed, but by how reliably they perform as one.
Agentic AI FAQs
Agentic AI refers to AI systems that can independently plan, make decisions, and execute tasks to achieve specific goals. In ITSM, AI agents can automate activities such as incident management, service requests, onboarding, problem resolution, and infrastructure operations while working alongside human teams.
Olympic success depends on teamwork, preparation, clear roles, and flawless coordination – not just individual talent. Similarly, successful Agentic AI deployments require AI agents to collaborate effectively, share context, follow governance rules, and operate within well-defined responsibilities rather than simply acting independently.
AI agents often perform specialized tasks within larger workflows. Without a shared business context, important information can be lost as work passes from one AI agent to another, leading to incorrect decisions, security risks, or compliance issues. Effective context sharing ensures every AI agent understands both the task and the business objective.
Continuous AI validation is the practice of regularly testing AI agents after deployment to ensure they continue to perform safely, accurately, and reliably. Rather than relying on a single round of testing before launch, organizations should continually evaluate AI agents against new threats, changing environments, and evolving business requirements.
AI agents operate in constantly changing environments. New cyber threats, prompt injection attacks, software updates, and business processes can all affect performance. Continuous testing helps organizations identify new risks before they impact IT services.
Each AI agent should have clearly defined responsibilities, decision boundaries, escalation paths, and ownership. This helps prevent duplicated work, conflicting actions, and gaps in service delivery when multiple AI agents collaborate on the same workflow.
As AI agents gain greater autonomy and access to business-critical systems, governance becomes essential. Organizations need clear policies defining which decisions AI agents can make independently, when human approval is required, and who remains accountable for business outcomes.
While AI agents can rapidly analyze data and recommend actions, strategic decisions involving business risk, regulatory compliance, financial impact, or organizational priorities should typically remain under human oversight. Governance frameworks help define these decision boundaries.
Successful Agentic AI relies on strong ITSM fundamentals, including:
Accurate configuration and service data
Well-integrated IT systems
Strong observability and monitoring
Clear workflows and processes
Robust security controls
High-quality knowledge management
Without these ITSM foundations, even advanced AI agents will struggle to deliver reliable outcomes.
Preparing for an AI workforce involves more than deploying technology. Organizations should establish governance, improve data quality, define AI operating models, continuously validate AI performance, clarify accountability, and ensure AI agents can securely share context across business workflows.
Traditional automation follows predefined rules and workflows. Agentic AI can reason, plan, adapt to changing situations, collaborate with other AI agents, and make decisions within defined boundaries, making it more flexible and better equipped to handle complex IT service scenarios.
The biggest lesson is that success comes from coordination rather than individual performance. Whether it’s athletes in a relay team or multiple AI agents supporting IT services, consistent success depends on preparation, communication, governance, clearly defined roles, and strong operational foundations rather than individual capability alone.
