Compare 50+ AI Agent Tools in August in 2025

Over the past few months, 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 and tools

Each of these platforms combines LLMs with tools and structured reasoning components like intent recognition, action execution, memory, context, and reflection.

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.

Use cases of AI agents

AI agents are used across many roles and industries. Below, I’ve listed some of the most common ways AI agents are being put to work:

• Developers
• SecOps assistants
• Human-like gaming characters
• Content creators
• Insurance assistants
• Human resources (HR) assistants
• Customer service assistants
• Research assistants
• Computer users
• AI agent builders

Note that some of these are agentic use cases, as Agentic AI encompasses and extends traditional AI agents by adding autonomy, memory, reasoning, and goal-directed behavior.

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