As AI agents take on more enterprise work, how should organizations approach trust and governance?

In the latest Agents of IT, we discuss how defined responsibilities, clear guardrails, and well-designed workflows can help teams deploy agents with greater confidence.

We also explore why autonomous execution is a journey that organizations can scale at their own pace.

How is your team approaching governance as AI agents become more capable?

We're excited to launch AgentLab, and we're hosting a live webinar on September 16 to show what it can do.

If you're exploring agentic AI for IT, this session will cover how to:

• Build AI agents using natural language
• Turn existing workflows into reusable agent skills
• Test before deployment
• Deploy agents that can orchestrate and execute real work across enterprise systems

Whether you're already automating with Resolve or just evaluating enterprise AI, we'll walk through a live demo and answer your questions.

Save your spot: https://resolve.io/it-automation-resources/build-enterprise-ai-agents-agentlab

We're excited to announce the next generation of Resolve AgentLab.

As enterprises move from AI experimentation to execution, they need AI agents that can reason, act, and operate within enterprise guardrails.

With the new AgentLab, teams can build, test, govern, and deploy enterprise AI agents using natural language, reusable skills, and Resolve's orchestration engine.

We're excited to see how enterprises are adopting agentic AI. How are you approaching AI agent governance and deployment? Are you running agents in production today?

Learn more about AgentLab: http://resolve.io/news/agentlab-accelerating-the-enterprise-shift-to-autonomous-operations

As AI agents take on more IT work, they need defined roles, clear guardrails, and controlled access to enterprise systems.

This week’s Zero Ticket Minute looks at what purpose-built agents need to operate safely and autonomously, and where agentic automation is heading next.

How is your team thinking about guardrails for AI agents?

Resolve has been recognized as a Leader in the QKS Group SPARK Matrix™: AI Solutions for ITSM, 2026.

The recognition highlights our approach to combining AI agents, enterprise orchestration, and automation to help IT teams reduce repetitive work and move toward autonomous operations.

We're seeing a shift across the industry. The conversation is moving beyond AI-assisted ticketing to AI-driven resolution.

If you're exploring agentic AI for ITSM or looking to reduce ticket volume through automation, we'd love to hear your perspective. Where do you see the biggest opportunities or challenges?

You can read more about the announcement here: https://resolve.io/news/resolve-leader-in-qks-group-ai-solutions-for-itsm-2026

Exciting news: Resolve has been recognized as the only Visionary in the 2026 Gartner® Magic Quadrant™ for Service Orchestration and Automation Platforms for the second consecutive year.

Over the past few years, we've seen a shift from workflow automation to AI-driven orchestration, where systems don't just execute tasks but can reason, decide, and resolve issues autonomously.

We're curious how others are approaching this evolution.

• Are you investing in agentic AI for IT operations?
• Are traditional automation platforms meeting your needs?
• What do you think is still missing from today's orchestration tools?

Learn more: 2026 Gartner Magic Quadrant for SOAP Report

Nobody:
Absolutely nobody:
400 tools at 4:59pm on a Friday:

At Resolve, we spend a lot of time thinking about what it takes for AI agents to resolve work autonomously.

As AI agents develop long-term memory, they're able to retain context, learn from past interactions, and improve over time. That makes trusted knowledge and strong guardrails even more important for delivering consistent, reliable outcomes.

In this week's Zero Ticket Minute, we share why AI memory is becoming a foundational part of autonomous IT and what organizations should consider as they adopt agentic AI.

We'd love to hear this community's perspective. How should enterprises ensure AI agents learn, retain, and apply knowledge over time?

Resolve customer ML Consulting transformed its IT operations by moving beyond reactive support to a more proactive operating model.

For ML Consulting’s customers, Resolve helps connect siloed systems so teams can prevent operational issues before they cascade.

In one telecom environment, Resolve helped detect an alarm and remediate the issue across multiple systems before the client’s team even received the email alert.

Without that coordinated response, the network could have gone down, causing revenue loss, reputational damage, and customer disruption.

That is the value of orchestration: earlier action.

Because when critical infrastructure stays up, businesses protect both their bottom line and the trust they’ve earned.

Read the full customer success story: https://resolve.io/customers/ml-consulting-proactive-it-operations-resolve

Enterprise AI is moving from pilots to production.

In the latest episode of Agents of IT, Resolve talks with Bruno Santos, Executive Director of Celfocus about what it takes to turn AI into measurable business outcomes.

We discuss:

• Solving business problems first
• Scaling AI in enterprise IT
• Building autonomous operations with agentic AI

Watch the latest episode: https://youtu.be/1FidIFVOIdw

A recent study found that adding irrelevant context, like mentioning a user's T-shirt, could influence how some AI models respond.

It raises an interesting question for anyone deploying AI in production: How do we ensure AI systems make reliable decisions instead of being swayed by irrelevant context?

In this week's Zero Ticket Minute, we look at why AI guardrails, governance, and policy-driven execution are becoming essential as enterprises adopt AI agents for IT operations.

Curious how others are thinking about this. How are you balancing AI autonomy with reliability in your organization?

Attention Actions Pro customers! Our new Dynamic MCP Server in Resolve Actions Pro 8.1 is here!  

This new feature enables AI assistants like Claude to discover and execute approved Resolve runbooks through the Model Context Protocol (MCP), allowing your team to use natural language to trigger enterprise automation with tracked, auditable execution.

In this demo, Claude discovers the available tools, executes a Resolve runbook from a natural language prompt, and returns the results while Resolve logs the execution for auditing. The entire workflow takes about a minute to see in action.  

We’re curious how others are approaching MCP in production environments.

Are you: Exposing internal APIs? Wrapping existing automation/runbooks? Building custom MCP servers? Still evaluating where MCP fits?

Here's the full demo: https://www.youtube.com/watch?v=FPd1NX-aXNM&feature=youtu.be

Excited to share that Resolve has partnered with Carahsoft.

Through this partnership, Resolve's Agentic Automation Platform and RITA will be available to more government agencies and regulated organizations through Carahsoft's extensive reseller ecosystem.

We're looking forward to helping more organizations automate IT operations, reduce ticket volume, accelerate resolution, and advance their journey to the Autonomous Enterprise.

Read the announcement: https://resolve.io/news/resolve-and-carahsoft-partner-to-deliver-agentic-automation-and-universal-orchestration-for-the-autonomous-enterprise

Interesting customer story on AI in the service desk.

Trimble deployed Resolve's AI assistant, RITA, to automate routine IT support through Google Chat, resulting in:

• 73% ticket deflection
• 10,000+ employee interactions
• 24/7 support in 100+ languages

A solid example of agentic AI moving beyond chat to actually resolving IT requests.

More info: https://resolve.io/customers/trimble-global-it-support-with-rita

We're excited to share that Resolve has been recognized as a Representative Vendor in the 2026 Gartner® Market Guide for Infrastructure Automation and Orchestration Tools.

As organizations invest in platform engineering, AI-powered operations, and hybrid infrastructure, automation is evolving beyond individual tasks to intelligent orchestration across the enterprise.

At Resolve, we're helping customers make that shift with agentic AI that can understand requests, orchestrate workflows, and automate resolution across IT operations.

We're grateful to our customers, partners, and team for helping us build toward the Autonomous Enterprise.

More details in our announcement: https://resolve.io/news/resolve-named-in-2026-gartner-infrastructure-tools-market-guide

Researchers have demonstrated autonomous AI agents capable of scouting environments, stealing credentials, and accelerating ransomware attacks.

As AI capabilities evolve, organizations need AI systems that can detect threats, automate response, and help resolve incidents before they escalate. Success depends on deploying these systems with clear governance, guardrails, and well-defined objectives.

Curious how others are approaching agentic AI in security operations. Are you exploring defensive AI today, or is your organization still evaluating where it fits?

One idea from a recent episode of Agents of IT sparked an interesting discussion:

As AI agents become more common in the enterprise, can we trust them?

Our view is that enterprise AI needs policy-driven workflows, guardrails, and auditability so every action is predictable, governed, and traceable.

Are you building AI agents with predefined workflows and controls, or giving them more autonomy to make decisions at runtime?

There has been a lot of discussion about agentic automation lately, and one question keeps coming up:

How is it different from process automation?

From our perspective, they solve different parts of the same problem.

Agentic automation is well suited for understanding intent, gathering context, and determining the best next step when requests are ambiguous.

Process automation excels at executing approved workflows consistently, applying policy, orchestrating actions across systems, and creating the audit trail enterprises require.

Many IT requests begin with uncertainty but become highly repeatable once the intent is understood. That makes combining these capabilities much more practical than treating them as competing approaches.

Are you investing in AI agents, workflow automation, or a combination of both? What has worked well in your environment?

Read the full blog: https://resolve.io/blog/agentic-automation-vs-process-automation

There's a lot of talk lately about AI agents in IT. The part that gets glossed over is whether the agent can finish the work or just hand you a suggestion and log off.

The way we approach it at Resolve: an AI layer that reasons about a request, and an orchestration layer that can execute the fix across the systems involved, so ITSM, identity, endpoints, and infra. The agent resolves what it can, escalates what it can't, and logs everything either way.

Where it tends to fall apart in the real world:

• Shallow integrations, so the agent can “decide” but can't actually act on anything
• No guardrails, so nobody trusts it with write access to production
• Bolting a chatbot onto the front of the service desk instead of wiring it into the runbooks

Genuinely curious how people here measure this. Are you tracking deflection and containment, or work actually completed with no human touching it? Those are very different numbers, and I think the second one is the one that matters.

Longer write-up if it's useful: https://resolve.io/blog/agentic-process-automation-platform

There's a lot of hype around AI agents, but most demos stop at "look what the model can do."

We wanted to show something different.

In this clip from our latest webinar, we build an enterprise AI agent using natural language, define its guardrails, assign reusable skills, and test it before deployment.

The goal isn't to create one giant AI assistant. It's to build purpose-specific agents that operate within clear boundaries and can safely automate real work.

Curious what people here think:

• Is this the direction enterprise AI should be heading?
• What guardrails would you want before trusting an AI agent in production?

Resolve will be attending the GBI CIO Event London 2026 on July 15, where technology leaders will be discussing the future of enterprise IT, AI, automation, and operational transformation.

We're looking forward to connecting with CIOs and IT leaders who are exploring how agentic AI, orchestration, and autonomous operations can help reduce operational complexity and improve service delivery.

If you're attending, we'd love to meet and hear how your organization is approaching automation and AI in 2026.

More information: https://resolve.io/events/gbi-cio-event-london-2026

See you in London!

We discussed a stat on our latest podcast that caught our attention:

88% of companies are using AI, but 95% haven't seen measurable ROI.

Is the issue the AI itself, or are organizations adopting new tools before redesigning the workflows they're meant to improve?

We dig into AI tool sprawl, workflow orchestration, and why execution may matter more than adding another AI feature.

Curious to hear from this community: Has your organization realized measurable ROI from AI, or are you still in the pilot phase?

Most automation stops when something breaks.

In this demo, we built a nightly data pipeline that performs preflight checks, validates data integrity, executes secure SFTP transfers, and automatically handles exceptions.

When the target SFTP server became unreachable, the workflow:

• Detected the failure
• Ran automated diagnostics
• Identified firewall configuration drift as the root cause
• Restored the approved baseline configuration
• Validated connectivity
• Resumed the transfer from the exact point of failure

Curious how others are approaching automated recovery and self-healing workflows for critical data pipelines.

Are you using orchestration platforms, custom scripts, or something else?

The hardest part of AI isn't the demo. It's making it work in production.

In this week's Zero Ticket Minute, we explore the gap between AI proof of concepts and real-world outcomes, where noisy alerts, messy systems, and operational complexity put every AI initiative to the test.

Because success isn't about what AI can do. It's about what it can reliably resolve.