Analyst(s): Mitch Ashley
Publication Date: October 20, 2025
A new generation of AI-native automation platforms is redefining workflow orchestration. AgentOps, the discipline of managing how AI agents coordinate work across tools and systems, is poised to rapidly overtake traditional procedural and rule-based workflow automation.
What is Covered in this Article:
- AgentOps is becoming the operational discipline for managing and governing intelligent digital agents.
- Vendors are blending AI-native innovations with legacy workflow technologies to enable hybrid automation environments.
- IT organizations and business departments are vital in maintaining workflow and process automation across productivity tools and systems.
- The evolution toward adaptive, context-aware automation marks a new phase of enterprise productivity infrastructure.
- AI agents are emerging as replacements for static rule-based automation tools such as Zapier and IFTTT.
The News: For over a decade, IT organizations and departmental teams across finance, marketing, and customer service have maintained a vital layer of the enterprise software ecosystem by building and maintaining workflow automations, integration scripts, and process tools that keep systems synchronized and business operations flowing. These local automations, often created through platforms like Zapier, IFTTT, Airtable, and custom scripting, have quietly powered digital productivity and operational efficiency.
The traditional “if this, then that” AI agent is overtaking the model of automation–based systems that interpret context, reason through tasks, and execute actions autonomously. This evolution is driving the emergence of a new operational discipline: AgentOps, which involves the management, governance, and optimization of intelligent agents that perform work across systems and teams.
AgentOps integrates AI reasoning models, orchestration layers, and context-aware data connections, allowing organizations to move beyond rigid automation chains toward continuously adaptive, self-optimizing workflows.
Futurum Research on AI, Agents and AgentOps
The 2025 Futurum Market Overview of Agentic AI Platforms in the Enterprise showed that 89% of surveyed CIOs emphasize agent-based AI as a strategic priority for increasing productivity and automating workflows in 2025 and beyond. Additionally, commercial solutions (e.g., Salesforce Einstein/Einstein Copilot, Microsoft Copilot Agents, Google Vertex AI Agents) lead to ease of deployment and integration, delivering ROI in as little as two weeks for automation use cases. For example, Microsoft Copilot Agents reduced response times for customer service by 30–50% in early enterprise deployments.
According to Futurum’s 2024/2025 DevOps and AI Platform Decision-Maker surveys, 12–18% of organizations report already having formalized AgentOps practices or dedicated tools. Most early adopters are concentrated in highly regulated industries, advanced AI labs, and digital-native technology companies. The top use cases focus on monitoring and managing AI agents performing automated customer support, business process automation, financial operations (FinOps), security and threat analysis, and R&D or co-pilot developer workflows.
Nearly half of large enterprises (45%) expect to pilot some AgentOps platform or workflow in the next 18 months as agentic systems mature. This data signals that AgentOps adoption is rapidly gaining traction as vendors bring agent-based solutions to market.
AgentOps: AI Agents Take Command of Workflow Automation
Analyst Take: The rise of AgentOps represents a clear break from static automation. Traditional workflow systems are rule-bound, requiring precise triggers and outcomes. AI agents operate differently as they evaluate intent, weigh data, and adjust dynamically, often collaborating with other agents or humans to achieve results. Automation will become a living system of adaptive digital workers that continuously learn and improve.
Several AI-native platforms now define this transition. Gumloop, Lindy, Relevance AI, VectorShift, and Relay.app enable users to design autonomous or semi-autonomous workflows spanning multiple systems and data sources. Gumloop merges data and live web intelligence for real-time research and content generation. Lindy orchestrates agents for personal and enterprise productivity, while Relevance AI and VectorShift extend agentic reasoning into analytics and business operations.
Enterprise automation leaders are also adapting. Automation Anywhere introduced Agentic Process Automation (APA), extending Robotic Process Automation (RPA) with reasoning capabilities. n8n, once primarily a visual workflow tool, now embeds AI agents integrated with large language models to interpret and adapt to live input streams.
Even established tools like Zapier, IFTTT, and Airtable are adding AI features. Zapier now supports natural language workflow creation, IFTTT adds context-aware triggers, and Airtable integrates AI-assisted automation to process unstructured data. These steps mark progress but not transformation, enhancing usability rather than enabling full agentic autonomy.
Similarly, Make (formerly Integromat) is expanding its visual automation builder into an AI-augmented orchestration environment. This will allow users to connect reasoning models, trigger dynamic decision logic, and create context-aware workflows that span SaaS applications and APIs.
Microsoft is rearchitecting its Power Platform around agentic operations by directly embedding Copilot and Agent Framework capabilities into Power Automate, Power Apps, and Fabric. Users can describe business outcomes in natural language and have the system generate multi-step workflows that span Microsoft 365, Dynamics 365, Azure services, and third-party applications. The integration of Copilot Studio enables organizations to design, deploy, and govern custom AI agents that adhere to enterprise compliance policies. This positions Power Platform as a low-code automation environment and an enterprise-grade AgentOps layer, connecting human intent, data intelligence, and operational execution across Microsoft’s ecosystem.
With watsonx Orchestrate, its flagship AI-driven automation and orchestration platform, IBM has also advanced into this space. Designed to unify and manage AI agents across business functions, watsonx Orchestrate enables technical and non-technical teams to build, deploy, and govern automations using no-code or pro-code tools. The platform includes over 100 domain-specific AI agents and over 400 prebuilt connectors that integrate with popular business applications. By offering centralized oversight and flexible deployment across hybrid cloud environments, watsonx Orchestrate represents IBM’s strategic push to operationalize AI at scale.
Google Gemini Enterprise is transforming Google Workspace into an AI-driven orchestration environment that embeds agentic intelligence directly into Gmail, Docs, Sheets, and Drive. Gemini functions as a workflow conductor that can summarize communications, extract data, generate documents, and trigger follow-up actions, all within context and across applications. Its ability to connect with CRM, ERP, and analytics platforms extends automation beyond Workspace, linking business data and communication channels into unified, context-aware processes. For enterprises, Gemini’s advantage lies in combining governance, data security, and real-time reasoning for corporate compliance requirements. I
Salesforce and ServiceNow are also embedding agents directly into their automation ecosystems. Salesforce is extending Einstein Copilot across Sales, Service, and Flow, enabling AI agents to act on CRM data and execute multistep workflows autonomously. ServiceNow is doing the same within its Now Platform, where AI agents can resolve IT tickets, automate service requests, and orchestrate end-to-end business processes. Both companies are effectively transforming their automation layers into governed agent ecosystems, connecting data, decisions, and execution in a single intelligent workflow fabric.
Atlassian also entered the agentic automation arena with its Rovo Agents platform, designed to manage intelligent workflows and service operations across IT, DevOps, and business teams. Rovo Agents unify automation, analytics, and collaboration through a shared AI layer that can plan, execute, and resolve tasks inside Jira, Confluence, and Bitbucket environments.
Salesforce, Service Now, Microsoft, Google, IBM, Automation Anywhere, Atlassian, Zapier, IFTTT, and Make demonstrate how established productivity and automation tools are layering AI capabilities to transition from static triggers to flexible, agent-driven orchestration.
How AgentOps Is Redefining Roles and Skills
Just as DevOps extended into DevSecOps, ITOps, FinOps, and DataOps, the rise of AI is creating new operational disciplines, most notably AgentOps, AIOps, and Agentic Infrastructure. These functions expand the scope of automation and introduce new responsibilities for managing digital agents across departments and workflows. AgentOps teams across IT and the broader organization now coordinate systems and infrastructure and the behavior, performance, and governance of AI agents operating across the business.
This evolution is reshaping job roles inside and outside IT. Operations professionals who once managed scripts, RPA tools, or process automations are increasingly becoming AI automation engineers. They orchestrate agents that integrate data, tools, and tasks across marketing, HR, finance, and operations. According to a related Fast Company post, “…instead of hiring a new content writer, content marketing teams might look for AI automation engineers with a strong eye for content. Instead of a new junior coder, engineering teams might look for an AI automation engineer with a technical background.” The same shift is visible across business units, where automation specialists and business analysts are learning to design, monitor, and optimize intelligent workflows rather than static ones.
This workforce transition underscores the central idea of AgentOps: automation is no longer about executing predefined actions; it’s about managing an ecosystem of reasoning systems that adapt to changing business needs. The emergence of these new roles represents a structural change in how organizations think about automation, workforce enablement, and digital productivity.
AgentOps Surges In Enterprises
For enterprises, the implications are significant. As automation networks evolve into collections of interacting agents, coordination, visibility, and governance become critical. Multiple agents can act concurrently across diverse systems, introducing risks of conflict or duplication if not managed. Organizations must establish oversight to ensure agents act predictably, safely, and in alignment with business objectives. Governance frameworks will define access control, escalation paths, and monitoring protocols. Observability will ensure accountability.
AgentOps delivers the operational framework to achieve this. It provides structure, governance, and feedback mechanisms that keep automation adaptive yet under control, spanning IT operations, finance reconciliation, marketing campaigns, and service workflows. The outcome is an enterprise automation ecosystem that continuously learns, optimizes, and collaborates.
Tech Vendor Guidance
Technology vendors face both an innovation opportunity and an adoption challenge. The shift to AI-driven, agentic automation is undeniable, yet most enterprises already rely on hundreds of workflow automations built on legacy RPA, integration, or no-code platforms. Replacing those automations wholesale is rarely cost-effective or practical.
Vendors must therefore innovate in hybrid mode, introducing agentic orchestration and AI reasoning capabilities that operate alongside existing rule-based systems. The most successful platforms will blend these paradigms, preserving some level of backward compatibility while increasingly aiding customers in ushering in autonomous, adaptive execution.
This hybrid approach is visible across several key ecosystems. Amazon’s strategy, centered on services like AWS Bedrock, Bedrock Agentcore, and the more recently announced Amazon QuickSight, integrates AI-driven analytics and visualization workflows with automation hooks spanning AWS and third-party systems. Microsoft Power Platform fuses low-code automation with generative reasoning via Copilot, bridging rule-based and agentic workflows. Google Gemini Enterprise injects contextual intelligence into workspace automations, allowing AI to reason across documents, communications, and operational data.
Oracle AI Data Platform, unveiled at Oracle AI World 2025, advances this blending of AI with existing data platforms to unify enterprise data, generative AI, and agentic automation in one environment. Built on Oracle Cloud Infrastructure and the Autonomous AI Database, it enables automated data ingestion, vector indexing, and integration with OCI Generative AI services to simplify how data is prepared and used by AI agents. The platform supports both developers and business users, providing tools to build, deploy, and manage AI-driven workflows that connect structured enterprise data with external sources.
Together, these moves demonstrate a maturing vendor strategy: innovation through interoperability, not disruption. Success in the AgentOps era will favor vendors that enable coexistence and innovation.
What to Watch:
- AI agents are replacing rule-based workflows as adaptive, context-aware automation becomes mainstream.
- Automation, RPA, and integration platforms are converging into unified AgentOps environments.
- Governance standards for agent behavior, accountability, and observability will emerge rapidly.
- Enterprises will experiment with hybrid human–agent collaboration models that reshape daily work.
- Cloud platforms from Amazon, Atlassian, Google, Microsoft, Oracle, Salesforce, and Service Now will accelerate AI automation and integration.
Disclosure: Futurum is a research and advisory firm that engages or has engaged in research, analysis, and advisory services with many technology companies, including those mentioned in this article. The author does not hold any equity positions with any company mentioned in this article.
Analysis and opinions expressed herein are specific to the analyst individually and data and other information that might have been provided for validation, not those of Futurum as a whole.
Other insights from Futurum:
Can Google Gemini Enterprise Unlock the Front Door for Business AI?
What Role Will Microsoft 365 Copilot Agents Play in Enterprise Workflows?
Agentic AI Expansion Across SDLC – Building Trust in AI
GenAI, Features, Are the Top Criteria for Future Enterprise Software Purchases
Author Information
Mitch Ashley is VP and Practice Lead of Software Lifecycle Engineering for The Futurum Group. Mitch has over 30+ years of experience as an entrepreneur, industry analyst, product development, and IT leader, with expertise in software engineering, cybersecurity, DevOps, DevSecOps, cloud, and AI. As an entrepreneur, CTO, CIO, and head of engineering, Mitch led the creation of award-winning cybersecurity products utilized in the private and public sectors, including the U.S. Department of Defense and all military branches. Mitch also led managed PKI services for broadband, Wi-Fi, IoT, energy management and 5G industries, product certification test labs, an online SaaS (93m transactions annually), and the development of video-on-demand and Internet cable services, and a national broadband network.
Mitch shares his experiences as an analyst, keynote and conference speaker, panelist, host, moderator, and expert interviewer discussing CIO/CTO leadership, product and software development, DevOps, DevSecOps, containerization, container orchestration, AI/ML/GenAI, platform engineering, SRE, and cybersecurity. He publishes his research on futurumgroup.com and TechstrongResearch.com/resources. He hosts multiple award-winning video and podcast series, including DevOps Unbound, CISO Talk, and Techstrong Gang.
