Compare 50+ AI Agent Tools in 2025

We have been conducting AI coding and tools use benchmarks to test the real-world capabilities of popular AI agents. In this article, we listed the best AI agent tools:

These AI agents are not yet fully autonomous; most serve as “co-pilots,” low-code LLM or logic-based tools where AI handles the heavy lifting of research and repetitive tasks execution, but with human oversight at key decision points.

Examples of popular agentic-style platforms

Each of these platforms combines LLMs with tools and structured reasoning components like intent recognition, action execution, memory, context, and reflection—core traits of agentic AI systems.

What is an AI agent?

An AI agent is more than just an LLM responding to prompts—it’s a looping system that uses LLM output to drive actions, track context, and continue reasoning until a goal is met.

Source: GitHub¹

However, there is no strict definition of what an “Agent” can be; it can be defined in several ways:

1. Traditional AI defines agents as — systems that interact with their environment.
   
2. Some analytics firms define agents as — fully autonomous systems that operate independently over extended periods, using tools such as functions or APIs to engage with their surroundings and make decisions based on context and goals.²
   
3. Others use the term to describe as — more prescriptive implementations that follow predefined workflows.³

Here are the factors that cause an AI system to be considered more agentic:

Here is a real-world example and conversation of an open source software agent managing deployments at Humanlayer:

Source: GitHub ⁴

Levels of agentic AI systems

Level 1. Rule-based automation (deterministic)

• At the most basic level, a coding assistant can generate code snippets in response to developer prompts.

Level 2. Strategic task automation

• A more capable agent can analyze an existing codebase and tailor its output accordingly — even writing code preemptively to satisfy a unit test once it’s been written.

Level 3. Context-aware and reflective agents

• A more agentic tool could not only write code but also compile and run it within a controlled test environment.

Level 4. Highly autonomous, adaptive systems

• Looking ahead, highly autonomous AI agents may be able to deploy fully tested applications to production through automated pipelines — triggered by natural language instructions and finalized with human approval.

Of note, listed agents are context-aware and reflective agents (level 3).

Capabilities of agentic AI systems

Adapted from: Cobus Greyling⁵

Read more: Enterprise AI agents, AI agent builders, large action models (LAMs), and agentic AI in cybersecurity.

AI agents: Use cases and real-life examples

1. Task automation

AI agents can undertake a variety of repetitive and time-consuming tasks. These agents can read and extract data from documents. You may instruct the agents to cross-check the extracted data against rules to ensure data correctness. This degree of precision makes AI agents suitable for monotonous jobs such as data input, processing, and invoicing management.

Real-life example:

Canon suffered a four-month backlog after using traditional invoice processing until it opted to use AI agents. They used 135 AI-powered UiPath robots to extract data from invoices, verify it, and enter it into accounting software.⁶

2. Resource optimization

Businesses may set up AI agents to collect data from many sources, including production logs, sales data, and customer communications. This helps to evaluate data to find patterns, trends, and anomalies that could optimize resource allocation.

Real-life example:

Avi Medical’s integrated multilingual AI agents automated 80% of patient questions, cutting median response times by 85%.⁷

3. Provide 24/7 omnichannel support

AI agents are available 24/7, guaranteeing that customers receive constant service. These smart assistants can interpret client requests and respond like people.

Real-life example:

Storytel employed an AI agent to roll out automated support in 13+ languages and counting in 109 languages. The company offers its customers more options for resolving their queries 24/7.⁸

4. Personalized service

AI agents use client data, such as purchase history and browsing activity, to provide tailored experiences. This learning technology enables AI agents to make suggestions and provide tailored replies to specific clients.

Real-life example:

Stitch Fix used agents to provide clothing recommendations to their customers, resulting in a reduction of 30% decrease in chat AHT.⁹

5. Effective issue resolution

AI  agents can collect and evaluate the necessary data to provide relevant solutions to frequent consumer inquiries. These agents can continuously improve their replies, resulting in effective service.

Real-life example: Bank of America employs a chatbot named Erica. The virtual agent does basic duties such as monitoring account balances and offering budgeting advice to customers, decreasing response times.¹⁰

6. Fraud detection

AI agents can track transactions in real-time to detect and prevent fraudulent activity. They can detect unusual activity and highlight possible fraud, reducing the expenses associated with fraudulent operations.

7. Optimizing inventory levels

Companies can optimize inventory levels with AI agents by leveraging demand prediction. 

8. Tailored real-time feedback

AI agents provide tailored feedback by assessing their replies and recommending areas for growth.

9. Content suggestions

AI agents suggest individualized information and resources depending on the learner’s progress and interests. It guarantees that the learning experience is relevant and interesting.

Explanations of AI agent specializations

1. AI agent builders

Agents that help users create and manage other agents.

Key features:

• Visual/no-code interfaces (e.g., flowcharts, block-based logic)
  
• Custom prompt chaining and memory management
  
• API integrations & action nodes (e.g., sending emails, querying databases)
  
• Role/playbook definitions for agents
  
• Multi-agent orchestration (e.g., supervisor-worker architecture)
  
• Real-time debugging, logging, and monitoring tools
  

2. Coding agents

Agents that assist with or automate software development.

Key features:

• Code generation, explanation, and debugging
  
• Integration with IDEs (e.g., VSCode extensions)
  
• Secure code practices (linting, CVE checks)
  
• Git operations (pull request creation, code reviews)
  

3. Web browsing agents

Agents that read and interact with web pages.

Key features:

• Action execution (clicks, forms)
  
• Web scraping with structured output
  
• Auto-research capabilities (summarizing pages, comparing results)

4. Customer support agents

Agents that handle support tickets, chats, and voice interactions.

Key features:

• Multichannel support (chat, email, SMS, phone)
  
• Contextual memory for long-term customer interactions
  
• CRM integrations (e.g., Salesforce, Zendesk)
  
• Escalation logic to human reps
  
• Sentiment analysis & auto-tagging
  
• Auto-resolution and knowledge base generation
  

5. Productivity agents

Agents that enhance or automate workflows and time management.

Key features:

• Calendar management (scheduling, rescheduling)
  
• Meeting summarization and note-taking
  
• Task prioritization and delegation
  
• Integration with Notion, Slack, Trello, etc.
  
• Auto-generated to-do lists from emails/notes
  

6. Marketing agents

Agents that handle content creation, SEO, and campaign management.

Key features:

• Copywriting for ads, blogs, emails
  
• A/B testing suggestions
  
• SEO optimization (keyword clustering, metadata generation)
  
• Social media post generation
  
• Analytics integration (Google Analytics, HubSpot)
  
• Persona-specific message crafting
  

7. Sales agents

Agents that drive lead generation, follow-ups, and pipeline management.

Key features:

• Cold email sequencing and follow-up
  
• CRM syncing (e.g., HubSpot, Salesforce)
  
• Real-time objection handling
  
• Meeting booking and reminders
  
• Deal pipeline summaries and alerts
  

8. HR agents

Agents that support hiring, onboarding, and employee management.

Key features:

• Resume screening and ranking
  
• Automated interview scheduling
  
• Onboarding checklist coordination
  
• Compliance check automation
  

9. Legal agents

Agents that assist with document generation, review, and compliance.

Key features:

• Contract drafting and redlining
  
• Clause extraction and comparison
  
• Legal citation checking
  
• Risk assessment and alerting
  
• Regulatory compliance mapping (e.g., GDPR, HIPAA)
  
• Confidentiality and NDA automation
  

10. AI deep research agents

Agents that specialize in literature review, analysis, and synthesis of research.

Key features:

• Paper summarization (arXiv, PubMed, SSRN, etc.)
  
• Auto-citation and bibliographic formatting
  
• Dataset/code extraction from publications
  
• Continuous update scanning (new papers, citations)
  

11. Healthcare agents

Agents for medical documentation, triage, and support.

Key features:

• Symptom checker and triage support
  
• EHR/EMR integration
  
• HIPAA-compliant data handling
  
• Medical dictation and transcription
  
• Prescription/dosage assistant
  

12. Cybersecurity agents 

Agents for monitoring, alerting, and remediation in security environments.

Key features:

• Threat detection (SIEM/SOAR integration)
  
• CVE patch recommendation
  
• Anomaly detection in logs
  
• Auto-response playbooks (e.g., isolate device, kill process)
  
• Risk scoring and prioritization

Further reading

• Compare 20 LLM Security Tools & Open-Source Frameworks 
• What are the Top 10 LLM Security Risks

External Links

• 1. GitHub - humanlayer/12-factor-agents: What are the principles we can use to build LLM-powered software that is actually good enough to put in the hands of production customers?.
• 2. AI Agents: What They Are and Their Business Impact | BCG.
• 3. AI Agents — Introduction, Workflows and Application | by Sulbha Jain | Medium. Medium
• 4. agents/deploybot-ts at main · got-agents/agents · GitHub.
• 5. 5 Levels Of AI Agents (Updated). 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀… | by Cobus Greyling | Medium. Medium
• 6. CIONET News | UiPath.
• 7. ”Avi Medical: Automating Healthcare and Customer Service“. Beam. 2024. Retrieved on July 28, 2024.
• 8. AI Agents – die autonomsten KI-gestützten Bots in der CX.
• 9. AI Agents — The Most Autonomous AI Powered Bots in CX.
• 10. Bank of America’s Erica Tops 1 Billion Client Interactions, Now Nearly 1.5 Million Per Day.