We’ve spent the past few months testing AI agents in real-world scenarios – not just reading marketing materials, but actually using these tools to see what works and what doesn’t.
Despite the hype around “autonomous AI,” most tools today are co-pilots, not autopilots. They handle research, automate repetitive tasks, and speed up workflows, but they still need humans to make key decisions.
We tested popular platforms across coding, customer service, sales, research, and business workflows. Some agents are specialized for specific industries like healthcare or sales. Others offer flexible frameworks you can customize for any use case.
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.
- n8n: Business workflow orchestration
- Tidio’s Lyro: SMB-centric agentic live chat
- Sully.ai: Healthcare research and workflow automation
- AiSDR: AI sales development
- Cursor: AI code editing
- Otter.ai: AI note-taking
- Averi: AI marketing content creation
- Make (Celonis): Scalable low-code automation
- Kompas AI: Deep research and report generation
- LangGraph: Production-grade complex agentic workflow generation
- Beam AI: Document-heavy workflows
- Relevance AI: Embedded analytics + decision flows
- IBM watsonx Orchestrate: Enterprise-grade orchestration
Read more
If you are looking into the infrastructure that powers web-capable agentic AI, here are our latest benchmarks:
- Remote browsers: How browser infrastructure enables agents to interact with the web securely.
- Browser MCP benchmark: Top MCP servers for tool use and web access.
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:
- Traditional AI defines agents as: Systems that interact with their environment.
- 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
- 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
Key differences in AI agent capabilities and design approaches
The diversity of AI agent tools reflects fundamental differences in their autonomy levels, architectural approaches, and specialization strategies. Understanding these distinctions helps organizations select the right tools for their specific workflows.
Autonomy and human oversight models
The most critical distinction lies in how much independence agents operate with:
Co-pilot agents (Level 3) like Cursor, Otter.ai, and Averi maintain human oversight at key decision points. They handle research and repetitive task execution but require approval before critical actions. This design prioritizes safety and control, making them suitable for high-stakes environments where errors are costly.
Strategic automation tools (Level 2) like n8n and Make (Celonis) follow predefined workflows with minimal real-time decision-making. They excel at reliable, repeatable processes but lack adaptive reasoning when encountering unexpected scenarios. Their deterministic nature ensures predictability but limits flexibility.
Rule-based systems (Level 1) represent the simplest form, responding to specific triggers without contextual understanding. While not truly “agentic,” these tools remain valuable for straightforward automation where variability is low.
The progression from Level 1 to Level 4 (highly autonomous systems) reflects increasing investment in memory management, context awareness, and reflection capabilities—each level requiring more sophisticated infrastructure and computational resources.
Domain specialization vs. general-purpose architectures
Agents optimize for either breadth or depth, creating distinct performance profiles:
Specialized agents like AiSDR (sales), Sully.ai (healthcare), and Tidio’s Lyro (SMB chat) embed deep domain knowledge into their design. They understand industry-specific workflows, terminology, and compliance requirements. This specialization enables higher success rates within their domain but makes them unsuitable for adjacent use cases.
Horizontal platforms like LangGraph, IBM watsonx Orchestrate, and Relevance AI provide flexible frameworks for building custom agents across domains. They sacrifice domain-specific optimization for versatility, requiring more configuration but supporting diverse use cases. LangGraph’s focus on production-grade workflow generation makes it powerful for developers building complex multi-agent systems, while watsonx Orchestrate targets enterprise-grade orchestration with governance and security built in.
Research-focused agents like Kompas AI optimize for deep analysis and synthesis, combining literature review capabilities with citation management and continuous monitoring. Their architecture prioritizes accuracy and thoroughness over speed, making them slower but more reliable for knowledge work.
Tool integration and ecosystem dependencies
How agents connect with existing systems significantly impacts their practical utility:
Native platform integrations distinguish business-focused agents. Tools like Beam AI (document workflows) and Relevance AI (embedded analytics) succeed by deeply integrating with common enterprise platforms—Salesforce, Slack, Notion, Google Analytics. Their value comes less from superior AI capabilities and more from seamless data flow between systems.
API-first architectures like n8n and Make enable custom integrations but require technical expertise to configure. These platforms support hundreds of pre-built connectors while allowing developers to add custom nodes, balancing flexibility with ease of use.
Standalone specialized tools like coding agents (integrated with IDEs) or cybersecurity agents (SIEM/SOAR integration) optimize for specific technical ecosystems rather than broad platform compatibility.
Security, compliance, and enterprise readiness
Production deployment requirements create major architectural differences:
Enterprise-grade agents like IBM watsonx Orchestrate and healthcare agents prioritize security certifications, audit trails, and compliance frameworks (GDPR, HIPAA, SOC 2). They implement role-based access control, data encryption, and governance workflows that consumer-focused tools omit. This infrastructure overhead increases costs but enables deployment in regulated industries.
Developer-centric tools like LangGraph and coding agents focus on debugging capabilities, logging infrastructure, and version control integration rather than enterprise compliance features. Their architecture serves technical users who can implement their own security measures.
Further reading
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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