We spent the last quarter testing AI agents across coding, customer service, sales, research, and business workflows. Not reading vendor marketing, actually using these tools daily to see what delivers and what does not.
Most tools today are co-pilots, not autopilots. They handle research and automate repetitive tasks, but still require human decision-making for anything that matters.
Examples of popular agentic-style platforms and tools
- Tidio’s Lyro: SMB-centric agentic live chat
- Creatio: Agentic CRM and AI Agent Builder for mid-size and large enterprises.
- Cursor: AI code editing
- Otter.ai: AI note-taking
- OpenAI Frontier: Enterprise agent management and orchestration
- Kiro (AWS): Spec-driven agentic IDE and autonomous coding agent
- 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 Watson Orchestrate: Enterprise-grade orchestration
What Is an AI Agent?
An AI agent loops. That’s the core difference from a chatbot.
Source: GitHub1
There is no single agreed-upon definition. Traditional AI defines agents as systems that interact with their environment. Some analytics firms define them 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 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:4
Source: GitHub 5
Capabilities of agentic AI systems
Adapted from: Cobus Greyling6
Read more: Enterprise AI agents, AI agent builders, large action models (LAMs), and agentic AI in cybersecurity.
Coding Agents
Cursor
Cursor remains the most widely adopted AI code editor among individual developers. In Reddit threads, even people who prefer other tools measure themselves against it. Its advantage is feel: smooth IDE integration built on VSCode, fast context switching between files, and a workflow that prioritizes speed over raw intelligence.
The 2026 release added parallel subagents for discrete subtasks, BugBot for automated PR-level code review,7 Cursor Blame (Enterprise) for per-line AI attribution, and image generation within the agent. Salesforce reported 30%+ velocity gains after deploying Cursor across 20,000 developers.8 Cursor has crossed $1 billion in annualized revenue with over a million paying developers.9
Where it struggles: Cursor’s pricing change, moving from 500 fixed monthly requests to a credit-based system tied to real API costs, created significant community backlash. The effective number of premium requests dropped from 500 to roughly 225 per month at the $20 price point. 10 Billing complaints still dominate discussions on r/cursor and G2. Plans currently range from $20/month (Pro) to $ 200/month (Ultra), with $ 60/month (Pro+) in between. Teams using heavy multi-file agent workflows should model their actual token spend before committing to a tier. Cursor is also less capable than Claude for architectural reasoning and can hallucinate on complex codebases.
Claude Code
Claude Code surpassed $2.5 billion in annualized run-rate revenue by February 2026, having doubled since the start of the year. It accounts for more than half of all enterprise spending on Anthropic products.11 Enterprises represent 80% of Anthropic’s overall business, and the number of customers spending over $100,000 annually on Claude has grown seven times in the past year.
Anthropic launched Claude Cowork, a macOS desktop agent built on Claude Code’s foundations for non-technical users. It uses folder-permission access, allowing Claude to read, write, and execute multi-step file tasks without command-line knowledge. The application was built by Claude Code itself in approximately 1.5 weeks. On January 30, Anthropic added a plugin system enabling department-level automation via custom MCP integrations, sub-agents, and slash commands.12
Anthropic launched Code Review for Claude Code, a multi-agent system that dispatches an AI team to analyze every pull request. The feature is in research preview for Team and Enterprise users. In Anthropic’s internal deployment, substantive PR comments increased from 16% to 54% after rollout.13 Less than 1% of findings are marked incorrect by engineers, and the system does not approve PRs; that decision stays with humans.
Anthropic also launched interactive apps directly inside the Claude chat interface, including Slack, Canva, Figma, Box, and Clay, enabling Claude to take actions inside these platforms without leaving the conversation.14
GitHub Copilot
GitHub Copilot underwent a major expansion in 2026, shifting from a code-suggestion tool to a multi-agent development environment. The January 14 CLI update introduced four specialized parallel agents: Explore (fast codebase Q&A without cluttering main context), Task (automated test and build execution with smart output summarization), and Code-review (surfacing logic and security issues, not style preferences). These agents run concurrently, compressing what previously required sequential handoffs into parallel execution.15
Kiro (AWS)
Launched in preview in July 2025, Kiro is a spec-driven agentic IDE that converts natural language prompts into structured requirements, technical design documents, and sequenced implementation tasks. At AWS re: Invent in December 2025, Amazon unveiled an expanded Kiro capable of working independently for days with persistent cross-session context, supported by an AWS Security Agent (identifies vulnerabilities as code is written) and a DevOps Agent.16
Amazon mandated internal adoption of Kiro over Claude Code, with approximately 70% of its software engineers having used Kiro at least once. However, roughly 1,500 Amazon engineers signed an internal forum post supporting Claude Code, citing Kiro’s performance shortfalls as a productivity impediment. This created a visible conflict: AWS sales engineers who sell Claude Code via Amazon Bedrock cannot officially use it in their own production work.17
Business Workflow Agents
OpenAI Frontier
OpenAI launched Frontier in 2026 as an open, end-to-end platform for enterprises to build, deploy, and manage AI agents across models from any vendor. HP, Intuit, Oracle, State Farm, Thermo Fisher, and Uber are among the first adopters. Frontier is OpenAI’s direct answer to IBM WatsonX, Orchestrate, Relevance AI, and Salesforce Agentforce in enterprise agent orchestration.
OpenAI deprecated its Swarm framework and launched a unified, provider-agnostic Agents SDK that supports 100+ LLMs, signaling a shift from experimental tooling toward production-grade infrastructure.18
Key capabilities: Defined agent identity with explicit permissions and role-based guardrails for regulated environments; built-in quality evaluation and feedback loops; a shared business context layer connecting data warehouses, CRMs, and internal apps; and a runtime deployable on-premise, on enterprise cloud, or OpenAI-hosted.19
IBM Watsonx Orchestrate
IBM Watsonx Orchestrate targets enterprise-grade orchestration, with built-in governance and security. It is designed for regulated industries where audit trails and compliance matter. The tradeoff is real: longer implementation timelines, higher cost, and a requirement to buy into the IBM ecosystem. For companies already running IBM infrastructure, this is the most defensible option. For everyone else, the overhead rarely justifies the choice.
Relevance AI
Relevance AI combines embedded analytics with decision flows. It succeeds by deeply integrating with common enterprise platforms, including Salesforce, Slack, Notion, and Google Analytics. Where horizontal platforms give you flexibility, Relevance gives you a faster path to deployment inside existing workflows.
Customer Service Agents
Tidio’s Lyro
Tidio’s Lyro focuses on SMB live chat with agentic capabilities. From real user reports: it handles 70-80% of common questions without human intervention and gets better with feedback over the first few months. It falls apart on questions requiring empathy or judgment calls. Not the right tool for complex customer situations.
Salesforce Agentforce
Salesforce Agentforce has become the dominant enterprise-grade customer service agent platform. Agentforce reached $800 million in annual recurring revenue, up 169% year over year. Salesforce has closed 29,000 cumulative deals since launch, with deal count growing 50% quarter over quarter.20 More than 60% of Agentforce bookings in Q4 came from existing customer expansion, which suggests the product is delivering enough production value for customers to expand rather than churn.
In a production deployment at UCSF Health, Agentforce Voice achieved 88% task coverage using simulation-based training, significantly above the 60-70% typical of traditional approaches.21
The broader pattern holds across platforms: customer service agents perform well on high-volume, repetitive inquiries and struggle with tasks that require judgment, empathy, or multi-party context.
Research and Analysis
Kompas AI
Kompas AI specializes in deep research and report generation. It actually reads and synthesizes academic papers, properly maintains citations, continuously monitors for new publications, and integrates with arXiv, PubMed, and SSRN. The tradeoff is speed: it optimizes for accuracy over throughput and costs more per query than general-purpose AI. For knowledge workers who need defensible, cited output, that tradeoff is worth it.
Beam AI
Beam AI handles document-heavy workflows, particularly in environments where structured data extraction from large document sets is the primary bottleneck.
Otter.ai
Otter.ai remains solid for meeting notes but has not evolved much beyond transcription and basic summarization since 2024. If that is all you need, it still works. If you need agents that act on meeting content, look elsewhere.
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.
What Differentiates Actually Useful Agents
Autonomy vs. Control
The biggest decision is how much independence you actually want. Co-pilot agents such as Cursor and Otter maintain human oversight at key decisions, handling research and execution but requiring approval before critical actions. Strategic automation platforms like n8n and Make follow predefined workflows with minimal real-time decision-making, which is predictable and reliable but breaks when encountering unexpected scenarios. Rule-based systems respond to triggers without contextual understanding, not really agentic but valuable for straightforward automation.
Most companies in 2026 operate at Level 2-3 agents. Full autonomy creates more problems than it solves unless you have built extensive guardrails.
Specialized vs. General-Purpose
Specialized agents embed deep domain knowledge. They understand industry workflows, terminology, and compliance requirements, achieve higher success rates within their domain, and are completely unsuitable for adjacent use cases.
Horizontal platforms such as LangGraph, watsonx Orchestrate, and Relevance AI provide flexible frameworks for building custom agents. They sacrifice domain optimization for versatility. LangGraph focuses on production-grade generation of multi-agent workflows, which is powerful for developers building complex systems but requires technical expertise. Relevance AI targets business users with pre-built templates and easier configuration. Research agents like Kompas AI optimize for accuracy and thoroughness over speed.
Integration Depth
Anthropic donated MCP to the Linux Foundation’s Agentic AI Foundation, making it a vendor-neutral open standard under the same independent governance model as Kubernetes and Node.js. MCP now has 10,000+ published servers and 97 million monthly SDK downloads, with first-class support across Claude, Cursor, GitHub Copilot, Gemini, VS Code, and ChatGPT.
Native platform integrations distinguish business-focused agents. Beam AI and Relevance AI succeed by deeply integrating with Salesforce, Slack, Notion, and Google Analytics. The value comes less from AI capabilities and more from seamless data flow. API-first architectures like n8n and Make enable custom integrations but require technical expertise, supporting hundreds of pre-built connectors while allowing custom nodes.
Security and Compliance
Production deployment requirements create major architectural differences. Enterprise-grade agents such as IBM WatsonX and healthcare agents prioritize security certifications (SOC 2, ISO 27001), audit trails, compliance frameworks (GDPR, HIPAA), role-based access control, data encryption, and governance workflows. That infrastructure overhead increases costs but enables deployment in regulated industries.
A notable real-world test of these limits: in February 2026, three US cabinet agencies directed staff to stop using Claude after Anthropic refused to remove contractual prohibitions on mass domestic surveillance and fully autonomous weapons.22 The episode illustrates that governance decisions made at the vendor level have direct operational consequences for enterprise customers in regulated or government-adjacent environments.
Developer-centric tools like LangGraph and coding agents focus on debugging, logging, and integration with version control systems, serving technical users who implement their own security. Consumer-focused tools often lack enterprise compliance features entirely.
The Governance Problem Nobody Solved Yet
Governance tooling is beginning to catch up. Several concrete solutions shipped:
- Cisco AI Agent Monitor for Splunk Observability Cloud real-time tracking of agent workflow quality, cost per run, and behavioral anomalies, entering public testing. 23
- OpenAI Frontier each agent is assigned a defined identity with explicit permissions, audit trails, and guardrails, modeled on how companies manage human employee access24
- Agentic AI Foundation (AAIF), OpenAI, Anthropic, and Block co-founded a Linux Foundation-backed consortium in December 2025 to establish open, vendor-neutral governance standards for agentic AI. AWS, Google, Microsoft, Bloomberg, and Cloudflare joined as Platinum members. Anthropic donated MCP to the foundation, ensuring it remains an open industry standard rather than a proprietary protocol25
What Works, What Doesn’t (Real Examples)
What Actually Works Today
Coding assistance at Level 3: Cursor + Claude Code combination used by thousands of developers. Cursor for flow and rapid iteration, Claude for hard problems.
Typical workflow:
- Use Cursor for 80% of coding (feature implementation, refactoring)
- When stuck, escalate to Claude Code for architectural reasoning
- Let agent run tests, iterate on failures
- Human reviews final output before merge
Sales outreach automation: AI agents qualify leads, book meetings, and send follow-ups. Companies report 2-3x increase in sales team productivity.
Klarna deployed sales agents handling initial outreach and qualification. Human reps focus on complex deals and relationship building.
Customer service for common questions: Agents handling 70-80% of routine inquiries during off-hours. Customer satisfaction scores improved because responses are instant instead of “we’ll get back to you tomorrow.”
Research synthesis: Academic researchers using agents to scan new papers, extract relevant sections, maintain citation databases. Saves hours of manual literature review.
What Doesn’t Work Yet
Fully autonomous deployment: Level 4 agents deploying code to production without human approval. Too risky for most companies. Even with extensive testing, edge cases cause problems.
Exception: Simple, well-bounded systems where failures are recoverable.
Complex customer situations: Agents fall apart when empathy, judgment, or nuanced understanding is required. “I understand you’re frustrated” from an agent feels hollow.
Multi-stakeholder decision-making: Agents can’t navigate office politics, understand unspoken context, or read between lines in business negotiations.
Creative strategy: Agents can execute tactics but don’t develop novel strategic approaches. They optimize within given parameters but don’t question the parameters themselves.
The Cost Reality
Everyone talks about agent capabilities. Few discuss economics.
Direct costs:
- Model API calls: $0.003-0.10 per 1K tokens (varies by model)
- Tool execution: APIs, data sources, integrations
- Infrastructure: Hosting, compute for self-hosted systems
Hidden costs:
- Context window usage accumulates fast with multi-turn conversations
- Failed execution attempts (agent tries, fails, retries you pay for each attempt)
- Debugging and refinement time
- Governance and security infrastructure
- Training team to work effectively with agents
Leading organizations treat agent cost optimization as first-class architectural concern. They build economic models into agent design rather than retrofitting cost controls after deployment.
Example optimization strategies:
- Route simple queries to smaller, cheaper models
- Use prompt caching aggressively (90% cost reduction for repeated context)
- Implement circuit breakers to stop runaway agents
- Monitor token usage per task, optimize prompts
- Batch requests when latency isn’t critical
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.
A structural shift is also underway in how vendors price agentic tools. Cursor’s move to a dual-pool credit system, and Anthropic’s bundling of Claude Code into Team plan seats, both reflect the market normalizing agentic AI as a line-item infrastructure cost rather than a per-query expense. Leading engineering organizations now model token spend at the workflow level, not per individual prompt.26
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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