What are AI agents?
AI agents are software tools that take a goal, plan the steps to reach it, and carry those steps out, often across other apps, files, or systems. A chatbot mostly answers questions. An agent acts: it can write and run code, send emails, update records, resolve support tickets, or run a multi-step workflow with little or no human input at each step.
The category is broad. It includes general-purpose "action engines" that handle open-ended tasks, workflow automation platforms that link AI models to thousands of apps, coding agents that read a codebase and open pull requests, and specialist agents for sales, customer support, research, scheduling, and data analysis. Many also give developers the building blocks, such as model APIs and agent frameworks, to create custom agents.
Key features to look for
- Autonomy and planning: Check whether the agent can break a goal into steps and run them itself, or whether you have to prompt it one step at a time. Some tools offer explicit plan-then-act modes so you can review the plan before the agent does anything.
- Integrations and tool access: An agent is only as useful as the systems it can reach. Look at which apps, APIs, databases, and communication channels it connects to, and whether it supports standards like MCP.
- Model flexibility: Some agents are tied to a single model provider. Others let you choose between models or bring your own API key, which affects cost, quality, and vendor lock-in.
- Human-in-the-loop controls: Approval steps, review queues, and permission settings let you decide what the agent can do on its own and what needs sign-off.
- Governance and access control: For teams, look for admin controls, audit trails, and IT-managed access, especially when agents touch customer data or production code.
- Deployment options: Cloud-hosted, self-hosted, local, browser extension, IDE, or terminal. The right surface depends on where your work already happens.
- Observability: Logs, run histories, and debugging tools make it easier to see why an agent did something and fix it when it goes wrong.
How to choose
Start with the use case. The best AI agents for one job are rarely the best for another. A software team needs a coding agent that understands a whole repository. A support team needs an agent that resolves tickets across channels. An operations team may be better served by a workflow automation platform. Narrow the list by job before comparing features.
Judge output quality on your own work. Run a trial on real tasks from your workflow rather than relying on demos. For coding agents, check whether the changes pass your tests and review standards. For sales and support agents, read the actual messages they produce.
Understand the pricing model. Most tools in this category are freemium, but the free tier limits often decide whether a tool is practical. Some support agents charge based on usage or resolved outcomes, so costs grow with volume. Estimate your expected usage before you commit.
Check workflow fit and integrations. An agent that lives in your existing editor, calendar, CRM, or workspace will get more use than one that needs a separate interface. Confirm it connects to the systems it needs to act on.
Review data handling and privacy. Agents often get broad access to email, code, documents, and customer records. Check where data is processed, whether it is used for model training, and what permission controls exist. Local or self-hosted options may suit teams with strict data requirements.
Notable tools in this category
- Manus: A freemium AI action engine built to execute tasks, automate workflows, and build apps rather than only answer questions.
- Lindy: A freemium AI teammate that connects to a company's tools to automate real work across teams.
- Zapier: A freemium no-code platform for connecting 9,000+ apps and orchestrating AI-powered workflows and agents.
- n8n: A freemium, fair-code workflow automation platform that lets technical teams build AI agents and integrations with code-level flexibility.
- Claude Code: Anthropic's freemium agentic coding tool that reads a codebase, edits files, runs commands, and ships fixes from the terminal, IDE, or Slack.
- Devin: A freemium autonomous AI software engineer that plans, codes, tests, and ships pull requests in the cloud or on a local machine.
- Fin by Intercom: A paid AI customer agent that resolves support and sales conversations across channels, with usage-based pricing tied to outcomes.
- Clay: Freemium GTM infrastructure that enriches data, runs AI research agents, and automates outbound at scale.
Pricing: what to expect
Freemium is the dominant model in this category. Most listed tools, including general agents, workflow automation platforms, coding agents, and developer APIs, offer a free entry point with paid tiers for more usage, features, or team controls. That makes free AI agents easy to try, though free plans usually limit volume, model access, or advanced features.
A smaller group is paid-only. These are mostly tools aimed at businesses and larger teams: Gong for revenue intelligence, Fin by Intercom for customer support, and Augment Code for engineering teams. Fin's pricing is usage-based and tied to outcomes, so the cost depends on how much work the agent handles.
One listed tool, Relay.app, has permanently shut down and is no longer an option for new workflows. When comparing plans, look past the headline tier and check how usage, seats, and model costs add up for your expected workload.
