Latest news with #helpdesk
Forbes
17-06-2025
- Business
- Forbes
The Future Of AIOps Is Many Agents Working Together
Karthik Sj, General Manager, AI at LogicMonitor. Built & Scaled multiple 0-1 AI products across public, PE and VC backed companies. If it feels like everyone is suddenly selling "AI agents," you're not imagining it. They're everywhere, pitched as automation 2.0: fewer tickets, faster resolution, more time for strategic work, replace the help desk, reinvent the NOC and free your team from toil. But behind the confident marketing is an open secret: There's no consensus on what an AI agent actually is. Definitions vary wildly depending on who's talking—and what they're selling. Some define agents as tools that use large language models. Others frame them as autonomous systems capable of observing, deciding and acting independently. In some documentation, "agent" is just a new label for a chatbot with plug-ins. In others, it implies a complex, goal-driven process manager. Even the terminology is unstable. Some use "agent" and "assistant" interchangeably. Others draw a hard line between them. A few acknowledge the ambiguity outright, admitting that the term can describe anything from rule-based workflows to reasoning systems. The term "agent" is being stretched past the point of meaning, making it harder for teams to evaluate real capabilities and turning every vendor demo into a guessing game. If "agent" is going to be the core metaphor for how AI works in the enterprise, it's worth getting clear not just what these systems do but how they do it, how they interact and where the limits actually are. So let's reset. At the most practical level, an AI agent is a system that can observe, reason, act and adapt in pursuit of a goal with as little human intervention as possible. That's it. The key isn't that it's "smart." It's that it has agency. The term "agent" comes from "agency": the capacity to take meaningful action toward a goal. Not to wait but to initiate. To sense the environment, decide and do. That's what gives agents their power. Most AI solutions marketed as 'agents' today fail that definition. They don't observe; they wait for prompts. They don't reason; they pattern-match. They don't act; they suggest. They don't adapt; they repeat. Strip away the marketing, and most are just prompt-driven wrappers around existing functionality. And that's fine—as long as we're honest about it. What matters is what the system can actually do. The next leap forward in AI for ITOps isn't building smarter standalone agents; it's engineering modular systems within which multiple specialized agents solve problems as a unit. Why? Because incidents in production environments rarely follow a script. They're layered. They span telemetry, infrastructure, services and human teams. They require context, judgment, escalation paths and institutional memory. A single agent can't handle that complexity. Take a real-world example: a business-critical application is running slowly. • A correlation agent clusters related alerts across infrastructure layers—VMs, databases and network components—into a single incident. • A diagnostic agent identifies the likely root cause: a dependency bottleneck between the application and its backend services. • A retrieval agent checks for a runbook, which includes manual steps, required approvals, system integrations and references to automation playbooks. • A remediation agent surfaces the most relevant playbook (e.g., reverse a config change, increase disk space, restart a failing collector), along with alternatives. The user confirms or selects the right one. • The orchestrating agent executes the playbook using automation tools. If no playbook exists, the system flags the issue for manual resolution and captures the pattern for future automation. • A verification agent checks whether the system has recovered and performance has normalized. • A summarization agent compiles the full incident timeline and updates the ticketing system and internal documentation. Each agent plays a distinct role—detection, reasoning, action, documentation—but none act alone. It's the orchestrator that routes requests, maintains context and sequences multiagent workflows based on the user's intent and the system state. With an agentic system, by the time you're alerted to a P1 incident, multiple agents may already be working the case: clustering alerts, identifying root causes, fetching past incident data and, in some cases, starting remediation. You're not beginning with a blank screen. You're stepping into an investigation already in motion. This is all to say that the obsession with 'smarter' agents is a distraction. You don't need a genius agent that can do everything. You need a suite of agents that know their role, play well and communicate with others. That means: • Specialization, not generalization • Communication protocols, not black boxes • Predictable behavior, not vague promises of 'autonomy' If that sounds familiar, it's because we've already solved this problem in the human world. It's how high-functioning teams work. When you're evaluating whether an 'AI agent' is real or just repackaged automation, ask: • Can your agents communicate with each other or only with humans? Good: 'Agents share structured context and trigger each other to take action based on system state.' Bad: 'Our agent sends alerts to Slack.' • How is work divided among agents? Good: 'We have agents for monitoring, diagnostics, remediation and notifications.' Bad: 'Our agent can handle any use case.' • What happens when things don't go according to plan? Good: 'Agents can pull in other agents or escalate dynamically.' Bad: 'Our agent was trained on millions of incidents.' If the vendor talks more about how smart the agent is than about how the system works, it's okay to be skeptical. If you're confused by the flood of AI agent announcements, rest assured you are not alone. The industry hasn't agreed on what 'agent' even means yet. Rapid innovation has led to rushed narratives, vague demos and a race to sound future-ready without doing the hard system design work. But this confusion is also a gift. It creates space to ask better questions—about what's real, what's useful and what's needed. The next phase of AI won't be won by whoever builds the smartest-sounding agent. It'll be led by teams who design agentic systems that can coordinate and specialize. That's the agentic AI worth building. Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?
TechCrunch
02-06-2025
- Business
- TechCrunch
Console raises $6.2M from Thrive to free IT teams from mundane tasks with AI
If you've ever been locked out of your work computer, you know the urgent need to reach IT support. Unfortunately, helpdesk staff are often busy assisting others, which can mean a significant delay before you regain access. Andrei Serban realized the critical importance of IT's manual work when he was a product lead on Rippling's apps and integrations team. Serban, who joined Rippling after it acquired his coding security startup Fuzzbuzz in 2023, saw an opportunity to automate many of the helpdesk's basic tasks with AI, from resetting passwords to granting access to apps like Figma and Miro, and routine troubleshooting. Sereban couldn't wait to get started on his vision. He left Rippling last year to found Console, a startup on a mission to help IT teams reduce mundane, repetitive tasks, thereby freeing up time for helpdesk professionals to work on more strategic and sophisticated projects. While trying to automate helpdesk functions isn't new, Console differentiates itself from existing competitors, including Moveworks, which was acquired by ServiceNow in March for $2.85 billion, by forgoing long, complex installation processes. Thanks to its easy integration with Slack, Console's AI assistant can be made available to everyone in the company in just a few weeks. 'We're able to get there so fast because you don't require you to replace your help desk,' he said. Console sees itself as an AI co-worker that uplevels existing helpdesk professionals. Serban even suggested that as companies digitize more of their operations, the IT function will become increasingly vital. Employees message Console on Slack and the startup's AI agent quickly responds to requests because it knows everything about the user, from their specific laptop model to the applications they have permission to use. Console's AI can resolve over 50% of the tasks on its own, and it will loop in someone from IT for more complex issues, Serban said. Techcrunch event Save now through June 4 for TechCrunch Sessions: AI Save $300 on your ticket to TC Sessions: AI—and get 50% off a second. Hear from leaders at OpenAI, Anthropic, Khosla Ventures, and more during a full day of expert insights, hands-on workshops, and high-impact networking. These low-rate deals disappear when the doors open on June 5. Exhibit at TechCrunch Sessions: AI Secure your spot at TC Sessions: AI and show 1,200+ decision-makers what you've built — without the big spend. Available through May 9 or while tables last. Berkeley, CA | REGISTER NOW The fast install time attracted customers, including Scale AI, Flock Safety, and Calendly. These customer wins have led Console to secure $6.2 million in seed funding from Thrive Capital. Vince Hankes, a partner at Thrive, said that the firm, which has backed many of the leading AI companies, including OpenAI, Cursor, and ElevenLabs, has had a thesis about artificial intelligence's potential to assist with IT tasks since ChatGPT was first released in the fall of 2022. While Hankes acknowledged that Console's fast integration might make it seem easily replaceable, he believes its AI technology will improve so quickly with user adoption that it will become an indispensable, deeply integrated system essential to everyone at the customer's company. Console's goal is to eventually start supporting other functions, including answering employee requests about HR, finance, and legal. 'We want Console to be an employee's first call for help,' Serban said.
