These top 30 AI agents deliver a mix of functions and autonomy

The practical takeaway for IT teams is that โ€œagentโ€ is not a single architecture. The mix described here spans chat-first copilots, browser-driven operators, and workflow engines that trigger from events. That means integration strategy matters more than branding: each class has different dependencies on identity, permissions, API coverage, browser control, and audit logging.

For architects, the main design question is where autonomy should stop. Turn-based assistants are easier to govern because the user remains in the loop, while event-driven enterprise agents can act without a human present once deployed. That shift changes the control plane: you need explicit guardrails for triggers, tool access, approval paths, rollback, and exception handling, especially when agents can modify records, send messages, or execute transactions.

Operationally, browser and GUI agents introduce a different failure mode than API-based automation. Their usefulness depends on the stability of the interface they are driving, which creates coupling to application layouts and fragile workflows. In practice, that raises maintenance overhead, testing requirements, and the risk that a minor UI change breaks an end-to-end process. Teams should treat these agents like production automation, not just an enhanced chatbot.

Security and governance become harder as autonomy increases. Background execution, event triggers, and direct transactions expand the blast radius of a misconfiguration or prompt injection. For enterprise rollout, the essentials are least-privilege access, segmentation of sensitive actions, human approval for high-impact steps, and logging that can reconstruct what the agent saw and did. Without that, agent adoption can outpace operational readiness.

 
Which autonomous or semi-autonomous agents are making the greatest impact on the world — and potentially your job — these days? Certain agents are hogging all the headlines lately, but there are a number of function-specific agents available to developers and users. MIT’s CSAILย — the university lab dedicated to AI research — set out to identify and document the background and capabilities of these agents, with its findings detailed in its latest AI Agent Index. The researchers conducted an ecosystem-wide analysis of state-of-the-art AI agents across 1,350 data points.

What is the functionality and origin of leading agents? The researchers found that interfaces are the most abundant, followed closely by enterprise workflow platforms. They also uncovered risks shared across these agents, as explored by my ZDNET colleague Tiernan Ray. Agents featured in the MIT index include the following:
  • Anthropic Claude/Claude Code
  • Google Gemini/Gemini CLI
  • Manus AI
  • OpenAI ChatGPT/ChatGPT Agent/Codex/AgentKit
  • Perplexity
  • Alibaba MobileAgent
  • ByteDance Agent TARS
  • Perplexity Comet
  • IBM watsonx Orchestrate
  • Microsoft 365 Copilot
  • SAP Joule Studio
  • Salesforce Agentforce
  • ServiceNow AI Agents
Here are the three leading categories of agents identified by the researchers:ย  ย 

Enterprise workflow agents (13 systems of the 30 systems covered):ย These are platforms with agentic features for automating business tasks. Examples include Microsoft 365 Copilot and ServiceNow Agent. Chat applications with agentic tools (12 systems):ย This category primarily includes chat interfaces with extensive tool access, according to the researchers. Examples include general-purpose coding agents such as Claude Code, as well as agents embedded in broader products such asย  Manus AI and ChatGPT Agent.   Browser-based agents (5 systems):ย These are agents whose primary interface is browser or computer use, with extensive browser/computer interaction tools. “They are distinct from chat agents with web search capabilities — ChatGPT web search, Claude web search — which primarily perform retrieval and summarization,” the researchers state. “Browser-based agents present higher risks through background execution, event triggers, and direct transactions.” Examples include Perplexity Comet, ChatGPT Atlas, ByteDance Agent TARS.

What are the most popular uses for AI agents?

Top use cases for AI agents, cutting across the above categories, include research and information synthesis, as seen in 12 of the 30 agents covered, spanning both consumer chat assistants and enterprise platforms. Right behind this functionality is workflow automation across business functions — such as HR, sales, support, and IT — enabled by 11 agents, primarily found in enterprise products. Agents focused on GUI or browser capabilities, used for tasks such as forms, ordering, and booking, are present across seven of the models.   Levels of autonomy vary considerably, the researchers found. Chat-first assistants maintain the lowest levels of autonomy. These are based on turn-based interactions, and include Anthropic Claude, Google Gemini, and OpenAI ChatGPT, which “executes a single set of actions and waits for the next user prompt.” On the higher end of autonomy, browser agents offer more “limited opportunities for mid-execution intervention.” These include Perplexity’s Comet, which performs tasks autonomously once prompted. “Once a query is sent, users cannot easily intervene or steer the agent until it finishes.” Enterprise platforms are split when it comes to agent autonomy. “During the design phase, users manually configure triggers, actions, and guardrails using visual canvases,” the researchers wrote. Others may offer AI assistance with this process. Once deployed, these agents often operate at higher levels of autonomy, “triggered by events like a new email or a database change, without any human involvement during the actual task execution.” Such agents include Glean, Google Gemini Enterprise, IBM watsonx, Microsoft 365 Copilot, n8n, and OpenAI AgentKit.   A few offerings are considered developer/command-line-interface (CLI) agents that require explicit confirmation for sensitive operations such as file edits and command execution. Some agents offer “watch mode” for real-time oversight of critical actions, including ChatGPT Agent/Atlas, and Opera Neon. Agent developers are concentrated in the US and China, with limited representation from other regions, the study also found.
These top 30 AI agents deliver a mix of functions and autonomy

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