Why AI agents and agentic AI matter for protecting data and business continuity
If you’re responsible for keeping systems secure and clients online, understanding the difference between agentic AI and AI agents can make all the difference. For MSPs and IT teams, these tools directly influence how quickly threats are detected, how smoothly recovery unfolds, and how resilient systems remain under pressure. Choosing the right approach can mean the difference between minimizing downtime and facing costly disruptions.
Automation and intelligence
AI agents streamline repetitive and predictable tasks, such as ticket routing, patch management, and password resets. While they reduce administrative overhead and ensure consistency, their value stops at automation.
Agentic AI goes beyond automation to deliver outcomes. Instead of just carrying out commands, it evaluates situations, makes context-aware decisions, and adapts in real time. In a disaster scenario, this means a system can re-sequence recovery steps, reallocate resources, and even communicate with stakeholders automatically, something a traditional agent cannot achieve.
Proactive threat detection
Most AI agents flag unexpected or anomalous activity and hand it off to a human operator. Agentic AI can analyze the same anomaly, determine its potential impact, and begin mitigation instantly.
For example, imagine a ransomware event. An AI agent may detect unexpected file encryption and send an alert. An agentic AI system would detect the same behavior, isolate affected systems, initiate backup restoration, and notify leadership, all without waiting for manual intervention.
Recovery orchestration
In BCDR, speed and coordination are everything. AI agents may restore a single database or trigger one workflow. Agentic AI models can coordinate the entire process: restore virtual machines, validate applications, spin up temporary infrastructure, redirect user traffic, and send automated updates to stakeholders.
This level of orchestration transforms recovery from an error-prone manual checklist into an intelligent, adaptive, and proactive process.
What to know before introducing agentic AI to your technology stack
While the benefits of AI agents and agentic AI are clear, MSPs and enterprise IT teams should also approach implementation with caution. Not every solution on the market delivers what it promises, and even the most advanced systems require the proper safeguards to avoid unintended consequences. Before committing to new tools, leaders need to be aware of two common pitfalls that can undermine efforts to optimize with AI.
Beware of “agent washing”
Agent washing is one of the biggest risks in today’s AI marketplace. The term refers to vendors overstating or mislabeling their products as “agentic” to ride the current wave of hype. In many cases, tools that only automate narrow, repetitive tasks are being marketed as intelligent, adaptive, or even autonomous without truly having those capabilities.
According to Gartner, only about 130 of the thousands of agentic AI vendors actually deliver on their promises. Most solutions still lack the maturity or agency to handle complex business objectives or interpret nuanced instructions over time.
Despite that, Gartner predicts that by 2028, agentic AI will autonomously handle around 15% of day-to-day work decisions and appear in roughly one-third of enterprise software applications, a sharp jump from nearly zero in 2024.
For MSPs and IT leaders, this means separating proof from promotion. Look for signs that a product genuinely adapts, rather than just automates. Watch for red flags such as:
- Claims of autonomy without evidence of goal-setting or adaptive decision-making
- Limited workflows marketed as “full orchestration”
- No transparency or audit trail to explain how the AI reaches its conclusions
At the end of the day, the real test is simple: ask for a live demo. See how the tool performs within real-world BCDR workflows, not just in isolated automation tests. The best solutions prove adaptability in action, not just on a slide deck.
Maintain a human and AI balance
Even the most advanced agentic AI cannot replace the experience, judgment, and accountability of skilled IT professionals and technicians. Overreliance on automation without human oversight creates risks in compliance, customer trust, and error handling. Best practices for balance:
- Keep humans in the loop for high-impact recovery decisions
- Use AI agents for repeatable automation, but apply agentic AI only where its autonomy adds measurable value
- Establish guardrails and clear intervention points so staff can override or adjust AI-driven actions when necessary
- Integrate AI agents and agentic AI model capabilities into disaster recovery plans and incident response plans to document their use and track downstream impact
Best practices for AI implementation
Adopting AI agents or agentic AI requires that you deploy these technologies in ways that align with your BCDR goals. For MSPs and IT teams, the difference between success and disappointment often comes down to how carefully the rollout is planned, tested, and scaled. The following best practices can help ensure that AI strengthens resilience instead of introducing new risks.
- Start with agents that have a well-defined scope: Begin with AI agents in low-risk areas such as ticket triage, backup checks, or patch automation. Before you build an agent, make sure your process is well-defined and measurable. These use cases deliver quick efficiency gains and build confidence with well-known, measurable outcomes.
- Pilot agentic AI in controlled workflows: Introduce agentic AI gradually, focusing on areas such as recovery orchestration or incident response where impact can be measured. Pilots allow teams to validate capabilities and refine guardrails before expanding adoption.
- Demand transparency from vendors: Insist on visibility into how AI models make decisions and adapt over time. Transparency ensures you can verify whether a system truly delivers adaptive intelligence or is just repackaged automation. Make sure you understand how your sensitive information is stored, and how it is isolated from other customers or tenants.
- Embed oversight and guardrails: Even advanced AI requires human-in-the-loop controls. By implementing audit logs, clear intervention points, and escalation paths, IT leaders can prevent minor errors from becoming major disruptions.
- Scale gradually with clear metrics: Expand the use of AI in steps, measuring improvements in uptime, recovery speed, and error reduction. A metrics-driven approach means scaling decisions are based on proven value rather than gut feeling.
Embracing AI for optimized BCDR with the ConnectWise Asio® platform
Understanding the differences between AI agents and agentic AI is essential, but knowing how to apply them within a unified technology strategy is where the real value emerges. For MSPs and enterprise IT teams, the ConnectWise Asio platform provides that foundation. Built on a shared data layer, Asio generates AI-driven insights through the Security Dashboard, leveraging automation and orchestration across your entire service delivery ecosystem.
This unified approach eliminates silos that often limit traditional AI tools. By consolidating data from PSA, RMM, security, and backup solutions, the platform allows AI models to operate with full context. The result is smarter automation, proactive threat detection, and streamlined recovery workflows that strengthen BCDR and stakeholder trust.
As AI continues to mature, MSPs and IT leaders don’t need to choose between efficiency and intelligence. With the Asio platform, you can harness both, deploying AI agents for repeatable automation while layering agentic AI to handle complex, multi-step recovery and continuity challenges. This combination not only reduces downtime but also positions your organization to take advantage of AI innovation as it evolves.
See how the ConnectWise Asio platform can help you take advantage of AI. See a live demo or get started now.
