Agentic AI Tools Explained: What They Are and How They Work
Last week, an open-source AI agent refactored 47 files across a codebase, wrote tests for each change, ran the test suite, and fixed the three failures it introduced. The developer who kicked it off was eating lunch.
Key Takeaways:
- • Agentic AI differs from chatbots — agents plan, execute multi-step tasks, use external tools, and self-correct without human intervention between steps
- • Replit Agent is the most beginner-friendly (plain English to deployed app); CrewAI and AutoGen require Python programming knowledge
- • Best for well-scoped tasks — agents work reliably on defined projects but tend to struggle with ambiguous or open-ended goals
- • Running agents locally carries risk — use sandboxed environments or non-critical projects until you understand how they behave
That is the pitch for agentic AI: tools that don't just answer questions but actually do things. They plan multi-step tasks, execute them, observe the results, and adjust course when something breaks. Unlike a chatbot that responds once and waits, an agent keeps going until the job is done or it gets stuck.
The reality is messier than that pitch suggests. Some of these tools genuinely save hours of work. Others burn through API credits while confidently producing garbage. The difference between the two comes down to what you're asking them to do and how much autonomy you give them.
We spent three weeks testing seven agentic AI tools on real projects — not toy demos — to figure out which ones actually work, where they break, and who should care.
What Is Agentic AI?
A regular AI chatbot works like a vending machine: you put in a prompt, you get a response. If the response is wrong, you rephrase and try again. The AI has no memory of what it did three messages ago and no ability to take action in the world.
Agentic AI adds three capabilities on top of that:
Planning
The agent breaks a high-level goal ("refactor the authentication module") into concrete subtasks (read the current code, identify what needs to change, make the changes, run tests, fix failures).
Tool Use
Instead of just generating text, agents can read files, write code, execute terminal commands, browse the web, call APIs, and interact with databases. They operate in your actual environment.
Self-Correction
When a command fails or a test breaks, the agent reads the error output and tries a different approach. The good ones do this reliably. The mediocre ones loop on the same mistake five times before giving up.
According to Gartner, by the end of 2028, roughly 33% of enterprise software applications will include agentic AI components, up from less than 1% today. McKinsey's research estimates that agentic AI could automate 25-40% of current knowledge work tasks. Those numbers deserve healthy skepticism — analyst projections for AI adoption have been aggressively optimistic before — but the direction is clear.
The tooling is maturing fast. A year ago, building an AI agent meant writing hundreds of lines of custom orchestration code. Now you can install a CLI tool and have an agent editing your codebase within minutes. Whether that is a good idea is a different question.
How We Tested These Tools
We evaluated each tool across three real-world scenarios over the course of three weeks in January and February. No cherry-picked demos.
Test Scenarios
- Bug fix in an existing codebase: We gave each tool a Next.js project with a known authentication bug (session token not refreshing after password change) and asked it to find and fix the issue.
- Feature implementation: Add a CSV export function to a dashboard that handles pagination, date filtering, and proper escaping of special characters.
- Multi-file refactor: Convert a set of 12 React class components to functional components with hooks, preserving all existing behavior and tests.
For each scenario, we tracked:
- Whether the task completed successfully without human intervention
- Total time from start to a passing test suite
- Number of retries or corrections the agent needed
- API cost (where applicable)
- Whether the output would pass a code review
For multi-agent frameworks (CrewAI, AutoGen), we also tested a fourth scenario: building a content pipeline where one agent researches a topic, another writes a draft, and a third reviews it for accuracy.
7 Agentic AI Tools Compared
CrewAI
crewai.comCrewAI is a Python framework for orchestrating multiple AI agents that work together. You define "agents" with specific roles (researcher, writer, reviewer), assign them "tasks," and let the framework handle coordination. It is the most popular open-source multi-agent framework on GitHub with over 25,000 stars.
In our content pipeline test, we set up three agents: a researcher that pulled data from web searches, a writer that composed a draft, and an editor that checked facts against the original sources. The pipeline ran end-to-end in about 4 minutes and produced a coherent 1,200-word article. The fact-checking agent caught two inaccuracies from the writer — which was genuinely impressive.
The coding tasks were a different story. CrewAI is designed for workflow orchestration, not direct code editing. We had to write custom tools for file reading and writing, and the agents struggled with maintaining context across a large codebase. The bug-fix task took 11 minutes and required manual guidance twice.
Strengths
- +Multi-agent collaboration actually works for content and research tasks
- +Clean Python API — defining agents and tasks feels natural
- +Model-agnostic: works with OpenAI, Anthropic, local models
- +Active community and frequent updates
Weaknesses
- -Not built for direct code editing — needs custom tooling for dev tasks
- -Debugging multi-agent failures is painful — hard to trace which agent went wrong
- -Token costs add up fast when agents pass long context between each other
G2 rating: 4.5/5 (28 reviews) | Pricing: Open-source (free), CrewAI Enterprise from $99/mo | GitHub: 25k+ stars
AutoGen (Microsoft)
microsoft.github.io/autogenAutoGen is Microsoft's open-source framework for building multi-agent systems. It takes a more academic approach than CrewAI — the documentation reads like a research paper, and the abstractions are more flexible but harder to learn. Version 0.4 (released late January) overhauled the architecture significantly, so older tutorials are mostly useless now.
AutoGen's standout feature is its conversation pattern system. You can define agents that debate, negotiate, or peer-review each other's work. In our content pipeline, we set up a "group chat" where the writer agent proposed sections, the reviewer challenged weak claims, and the writer revised. The back-and-forth produced noticeably better output than a single-pass pipeline — but it also used 3x the tokens.
For our coding tasks, AutoGen with its code executor agent performed respectably on the bug fix (solved it in about 8 minutes) but struggled with the multi-file refactor. It kept losing track of which files it had already converted.
Strengths
- +Most flexible conversation patterns of any framework we tested
- +Built-in code execution with Docker sandboxing
- +Microsoft backing means long-term maintenance is likely
Weaknesses
- -Steep learning curve — documentation assumes research-level familiarity
- -v0.4 breaking changes make community examples outdated
- -Multi-turn debates consume tokens aggressively
- -Setup is verbose — even simple workflows need 50+ lines of boilerplate
Pricing: Open-source (free) | GitHub: 38k+ stars | Requires: Python 3.10+
Cursor Agent Mode
cursor.shCursor's Agent Mode (introduced in early January) turns the AI code editor into something closer to an autonomous developer. Instead of just suggesting code completions or answering questions, Agent Mode can read your codebase, create and edit multiple files, run terminal commands, and iterate until things work.
This was the strongest performer in our coding tests. The bug fix took under 4 minutes — it read the auth module, identified the stale session issue, patched it, and ran the existing tests to confirm. The CSV export feature took about 15 minutes and the code was clean enough to merge with minor style adjustments.
The multi-file refactor is where things got interesting. Cursor Agent completed 10 of 12 component conversions correctly. The two failures involved components with complex lifecycle methods that didn't map cleanly to hooks. It flagged both as needing human review rather than guessing — a behavior we appreciated.
Strengths
- +Works inside a real IDE — full codebase awareness, not just single files
- +Can run terminal commands and react to output (build errors, test results)
- +Knows when to stop and ask for human input
- +Familiar VS Code interface lowers adoption friction
Weaknesses
- -$20/mo subscription required, plus you may hit usage limits on heavy agent sessions
- -Agent mode is still marked as beta — occasional crashes during long sessions
- -Limited to coding tasks — not useful for research or content workflows
G2 rating: 4.7/5 (84 reviews) | Capterra: 4.6/5 | Pricing: $20/mo Pro, $40/mo Business
For a full breakdown of Cursor's features beyond Agent Mode, see our Cursor Pro review.
Claude Code
Anthropic CLIClaude Code is Anthropic's terminal-based AI agent. You install it via npm, run it in your project directory, and it can read files, write code, execute shell commands, and manage git operations. Think of it as a senior developer who lives in your terminal.
In our bug fix test, Claude Code found the authentication issue in about 3 minutes. It read the relevant files, traced the session flow, identified the missing token refresh call, patched it, and created a commit. What stood out was the reasoning — it explained why the bug existed, not just what to change.
The multi-file refactor went smoothly until component number 9. Claude Code hit a context window limit and started losing track of earlier changes. We had to break the task into smaller batches. Once we did that, it completed everything cleanly. The CSV feature task took about 12 minutes — slightly slower than Cursor but with more thorough error handling and edge case coverage.
Strengths
- +Excellent reasoning — explains decisions, not just outputs
- +Strong at planning complex multi-step tasks
- +Permission system asks before destructive operations (git push, file delete)
- +Works in any terminal — no IDE lock-in
Weaknesses
- -Requires a Claude Pro or API subscription — can get expensive for heavy use
- -Context window limits hurt on large codebases — need to chunk work
- -Terminal-only UI means no visual diff review
Pricing: Requires Claude Pro ($20/mo) or API access | Platform: macOS, Linux, Windows
OpenClaw
Open-sourceOpenClaw is the open-source AI agent that exploded in popularity in late January. It runs entirely on your machine, uses your existing AI subscriptions (Claude, ChatGPT, Gemini) as the brain, and can access your file system, terminal, and browser. No cloud dependency for the agent logic itself.
Performance depends heavily on which model you connect. With Claude as the backend, OpenClaw handled our bug fix in about 5 minutes and produced solid code. With GPT-4o, it was slower (around 8 minutes) and the fix was correct but less elegant. With Gemini 2.0 Flash, it was fast but made a subtle error in the session logic that would have caused issues in production.
The real appeal is cost. If you already pay for Claude Pro or ChatGPT Plus, OpenClaw adds agent capabilities for free. You are not paying a separate subscription for the agent framework — just using the AI subscriptions you already have.
Strengths
- +Free and open-source — bring your own AI subscription
- +Runs locally — your code never leaves your machine
- +Model-agnostic: swap between Claude, GPT, Gemini freely
- +Rapidly growing extension ecosystem
Weaknesses
- -Quality varies wildly depending on the underlying model
- -File system access with limited guardrails — can accidentally modify wrong files
- -Still rough around the edges — error messages can be cryptic
- -Community support only — no SLA, no guaranteed response times
Pricing: Free (open-source), requires AI subscription ($20/mo Claude/ChatGPT/Gemini) | GitHub: 40k+ stars
We wrote a detailed review of OpenClaw including setup costs and security considerations: OpenClaw Review: The AI Assistant That Lives on Your Machine.
Replit Agent
replit.comReplit Agent is the most accessible option on this list. You describe what you want in plain English, and it builds the entire application — backend, frontend, database, deployment. No local setup, no terminal, no configuration files.
For our test scenarios, Replit Agent was a mixed bag. It could not work with our existing Next.js codebase (it builds new projects from scratch), so we skipped the bug fix and refactor tests. For the CSV export feature, we described it as a standalone app, and Replit Agent built a working Flask application with download functionality in about 9 minutes.
The code quality was acceptable but not great — inline styles, no type hints, minimal error handling. It deployed automatically to a Replit URL, which is convenient. But if you want to move the code elsewhere, the export process strips out Replit-specific configurations and sometimes breaks imports.
Strengths
- +Zero setup — describe and deploy, all in browser
- +Genuinely useful for non-developers building internal tools
- +Built-in hosting eliminates deployment complexity
Weaknesses
- -Cannot work with existing codebases — new projects only
- -Code quality is consistently mediocre — fine for prototypes, risky for production
- -Locked into Replit's ecosystem unless you export (which is clunky)
- -Ignores your tech stack preferences — picks its own frameworks
G2 rating: 4.4/5 (126 reviews) | Capterra: 4.5/5 | Pricing: $25/mo Replit Core
Devin
cognition.aiDevin markets itself as "the first AI software engineer" and attracted massive attention (and skepticism) when it launched. It runs in its own cloud sandbox with a full development environment — editor, terminal, browser — and you interact with it through a Slack-like chat interface.
We gave Devin our bug fix task by linking the GitHub repository. It cloned the repo, explored the codebase, found the authentication issue, and submitted a pull request in about 18 minutes. The fix was correct. The PR description was detailed. It even added a regression test we hadn't asked for. On the surface, this looked like the most "complete" result of any tool we tested.
But the multi-file refactor exposed Devin's limitations. It converted 8 of 12 components successfully, introduced a subtle state bug in two others, and silently skipped the remaining two without explanation. The PR it opened looked polished, but a careful review revealed issues that would have slipped into production if we had trusted the output blindly. At $500/month, the margin for error needs to be lower than that.
Strengths
- +Full sandboxed environment — cannot accidentally break your local machine
- +GitHub integration is seamless — clones, branches, opens PRs automatically
- +Produces professional-looking PR descriptions and documentation
Weaknesses
- -$500/month starting price puts it out of reach for most individuals and small teams
- -Silently skips tasks it cannot complete instead of flagging them
- -Output looks more polished than it actually is — the PR sheen can mask real bugs
- -Slower than local tools — cloud sandbox adds latency to every operation
Pricing: $500/mo (Team plan) | Platform: Cloud-based | Waitlist may apply
Comparison Table
| Tool | Type | Pricing | Best For |
|---|---|---|---|
| CrewAI | Multi-agent framework | Free (open-source) | Content pipelines, research automation |
| AutoGen | Multi-agent framework | Free (open-source) | Complex agent conversations, academic research |
| Cursor Agent | IDE agent | $20/mo | Developers who want IDE-integrated coding agent |
| Claude Code | CLI agent | $20/mo (Claude Pro) | Terminal-native developers, complex reasoning tasks |
| OpenClaw | Local agent | Free + AI sub | Privacy-conscious developers, budget-friendly option |
| Replit Agent | Cloud agent | $25/mo | Non-developers building prototypes |
| Devin | Cloud agent | $500/mo | Enterprise teams with budget for autonomous coding |
How to Choose the Right Agentic AI Tool
The agentic AI space splits into two distinct categories, and picking the right tool starts with understanding which category fits your work.
If you need an agent that writes and edits code
Cursor Agent Mode is the strongest option for developers who want an agent embedded in their workflow. It had the highest success rate across our coding tests and the IDE integration means you can review changes in real time.
Claude Code is the better choice if you prefer working in the terminal or need the agent to handle complex reasoning chains. Its explanations are more thorough, and the permission system gives you more control over what it can do.
If you want to orchestrate multiple agents
CrewAI is the pragmatic choice. Faster to learn, simpler API, and the community has built hundreds of example workflows. Start here unless you have a specific reason not to.
AutoGen is the better pick if you need sophisticated agent interaction patterns (debate, negotiation, hierarchical delegation) and you are comfortable with a steeper learning curve.
If you are not a developer
Replit Agent is the only tool on this list that requires zero technical setup. It will not produce production-grade code, but it can build functional prototypes and internal tools that actually work. For founders validating ideas or teams building simple internal dashboards, it removes a real barrier.
If budget is the primary concern
OpenClaw wins. The agent framework is free, and if you already pay for Claude Pro or ChatGPT Plus ($20/month), you are set. The trade-off is that you are responsible for quality — OpenClaw is only as good as the model behind it and the prompts you give it.
An honest caveat:
None of these tools eliminate the need for code review. Every single one produced at least one bug during our testing that would have made it into production if we had not reviewed the output carefully. Agentic AI is a productivity multiplier, not a replacement for engineering judgment. The teams getting the most value from these tools are the ones that treat agent output the same way they treat a junior developer's pull request: with careful, constructive review.
Related Reading
If you are evaluating AI coding tools more broadly (not just agentic ones), see AI Coding Tools Compared: 7 Options Tested.
For a deep dive into vibe coding — the adjacent trend where AI builds entire apps from prompts — read Vibe Coding Explained: 5 Tools That Turn Ideas Into Apps.
Interested in free options? Our Cline review covers the open-source VS Code extension that offers agent-like capabilities at no cost.
Frequently Asked Questions
What is the difference between agentic AI and a chatbot?
A chatbot responds to one prompt at a time and waits for the next instruction. An agentic AI tool can break a goal into subtasks, execute them autonomously across multiple steps, use external tools (file systems, APIs, browsers), and course-correct when something fails — all without human intervention between steps. The key distinction is autonomy: chatbots react, agents act.
Are agentic AI tools safe to run on my machine?
It depends on the tool and how much access you grant. OpenClaw and Claude Code run locally and can access your file system, terminal, and network. Both have permission systems that ask before executing potentially destructive commands, but mistakes happen. Devin runs in a cloud sandbox, which is safer for your local environment but means your code is processed on external servers. Running agents in a sandboxed environment or on non-critical projects is strongly recommended until you understand how they behave.
Which agentic AI tool is easiest for beginners?
Replit Agent is the most beginner-friendly option. You describe what you want in plain English, and it builds, deploys, and hosts the application without you touching a terminal. Cursor Agent Mode is the next step up — it requires basic IDE familiarity but handles most of the complexity. Frameworks like CrewAI and AutoGen require Python programming knowledge and are aimed at developers building custom agent workflows.
Can agentic AI tools work together in a pipeline?
Yes, and that is exactly what multi-agent frameworks like CrewAI and AutoGen are designed for. You can define multiple specialized agents — a researcher, a writer, a reviewer — and have them collaborate on a task sequentially or in parallel. The orchestration layer handles passing context between agents and resolving conflicts. In practice, these pipelines work well for content and research tasks but still struggle with complex coding workflows where agents need to share large amounts of context.
Last updated: February 17, 2026 | Published by OpenAI Tools Hub Team