What are AI agents?
An AI agent is a software system that uses artificial intelligence to carry out complex tasks on your behalf as an ongoing, goal-oriented process. What sets AI agents apart from traditional bots or automations is their ability to plan, decide, and act with minimal oversight.
An AI agent combines natural language processing (NLP) with decision-making, enabling it to follow your instructions across different apps and services.
Unlike simple AI tools that only chat or create content, autonomous agents can book meetings, summarize documents, fill forms, update your CRM, and run other multi-step workflows based on your goals.
From a technical perspective, this agentic shift requires orchestrating multiple steps, handling ambiguity, and reacting to feedback – something rule-based bots or simple automations don't do.
The rise of AI scheduling agents, AI calendar agents, AI assistants, and custom agents reflects this shift. We're entering a world where different parts of business processes have their own intelligent agents running quietly in the background.
What are AI agent platforms?
An AI agent platform is the infrastructure layer that allows organizations to create agents and deploy them at scale. Rather than focusing on a single complex task (like a calendar assistant), AI agent platforms orchestrate multiple agents and help you:
- Spin up custom agents with distinct roles, memory, and permissions
- Route tasks to different agents based on user input or system events
- Enforce governance, auditability, and access control
- Monitor agent performance, behavior, and cost
- Connect agents to internal tools, CRMs, databases, APIs, and more
AI agent platforms are critical for organizations that want to avoid shadow AI usage and safeguard agent development with the same controls you'd expect from SaaS or API platforms.
7 best AI agents and AI agent platforms in 2026
These AI tools stood out in expert conversations for reliability, depth of integration, AI security posture, and real-world results across different verticals.
This overview reflects our team's experience as of early 2026. Given the fast pace of AI tool development, keep in mind that AI agent performance characteristics change frequently.
Devin AI
Best for: Developers, tech leads, engineering managers
Category: AI coding agent
Devin AI is the closest thing we have today to a junior engineer who never sleeps. It understands entire repositories, plans development tasks, writes and debugs code, runs tests, and navigates live development environments.
Its architecture combines long-context LLMs, tool calling, sandboxed code execution, and task planning, making it a very practical programmable teammate within the development workflow.
Key strengths:
- End-to-end handling of coding tasks
- Strong planning and decomposition of complex tasks
- Effective for bug fixes, scaffolding, and unit testing
Limitations:
- Requires well-scoped objectives
- Not safe for unsupervised production code merges
- IDE integration varies by environment
If you need a specialized AI agent for software development that can operate safely within real-world constraints, Devin sets the current bar.
Lindi
Best for: UX designers, product teams, design operations
Category: Virtual AI agent
Lindi is a task-specific AI agent designed to support UX and product design workflows. It specializes in helping teams turn requirements into interface flows, UI patterns, and early-stage mockups that align with design systems.
Lindi's strength lies in its ability to work within real-world constraints. Once configured to reflect your brand's standards, patterns, and visual rules, it can generate structured wireframes, suggest layouts, enforce consistency, and assist with user research synthesis.
It also integrates directly with design tools like Figma, allowing teams to move from AI‑generated flows to editable designs without switching context.
Key strengths:
- Purpose-built for product and UX teams
- Maintains consistency with brand or design system rules
- Speeds up early-stage design work with structured outputs
Limitations:
- Narrow scope: not suitable for general business or automation tasks
- Requires upfront configuration to reflect your team's visual language
If you need an AI agent that actually understands interface design, Lindi is worth a look. It helps you move faster from product requirements to structured, production-ready design flows.
Antigravity
Best for: Engineering teams, AI infrastructure teams, toolchain developers
Category: AI agent platform
Antigravity is a new AI agent development platform introduced by Google in late 2025 that rethinks how software is built by embedding autonomous AI agents directly into the coding environment. AI agents plan, execute, test, and validate software tasks across the editor, terminal, and browser.
Antigravity transforms the development environment into a mission control for multi-agent workflows. Developers define high‑level outcomes, and the platform's multiple AI agents break those outcomes into actionable steps and manage tool interactions.
Antigravity supports multiple AI models, including Gemini 3, Claude, and open-source options. It wraps every task in Artifacts (structured outputs like plans, test logs, screenshots, and intermediate results), giving teams visibility into AI agent behavior.
Key strengths:
- Agents execute across code, shell, and browser
- Tasks produce traceable Artifacts for review
- Supports multi-model and multi-agent workflows
Limitations:
- Still in public preview
- Requires human oversight for production work
Antigravity isn't a calendar agent or AI scheduling agent. Its value lies in how it makes agents operationally useful within technical workflows. It marks a serious shift toward agentic development, where engineers define goals and autonomous agents handle execution inside a governed, observable workspace.
Claude Code
Best for: Developers, engineering leads, technical teams
Category: AI coding agent
Claude Code is Anthropic's agentic coding assistant, built into the Claude 3 model family. It understands and works with codebases at scale, helping engineers write, review, and modify code through natural language commands. Rather than offering isolated suggestions, Claude Code maintains context across multiple files and workflows, making it suited for real software tasks.
It can be used directly from the command line or integrated into IDEs and developer workflows, allowing teams to delegate routine engineering tasks while maintaining human oversight.
Key strengths:
- Maintains deep context across projects and files
- Accessible from terminals, IDE extensions, and web interfaces on some plans
Limitations:
- Requires integration into your development workflow
- Doesn't run entire pipelines or deployments without explicit commands
- Outputs still benefit from human validation, especially for production code
Claude Code brings contextual intelligence and multi‑file reasoning to practical software development – a substantial productivity boost when used with disciplined oversight.
nexos.ai
Best for: Mid-to-large enterprises deploying internal AI assistants at scale
Category: Unified AI platform with AI agents
nexos.ai is one of the few platforms that not only lets you deploy agents – it also provides its own suite of secure, business-grade agents designed to work on complex tasks within enterprise workflows.
What sets nexos.ai apart is that it combines agent delivery with a full AI orchestration infrastructure:
- A central AI Gateway for routing prompts and tool calls across systems and models
- A secure AI Workspace for multiple LLMs where teams can interact with agents under access controls
- AI Governance tools to manage usage, enforce policies, and monitor risk
- Full LLM observability: monitor outputs, token usage, tools, and agent behavior
Key strengths:
- Provides prebuilt agents plus tooling to build your own AI agents
- Role-based access control and audit trails baked in
- Multi-model support (Anthropic, OpenAI, Mistral, etc.) with AI guardrails and the ability to compare AI models
- Central dashboard for enterprise-grade security, usage tracking, and AI model performance
Limitations:
- Requires organizational buy-in and setup to fully benefit
- More infrastructure than interface – best for structured teams
nexos.ai is a control plane for enterprise AI. If you need to deploy multiple agents across your organization while maintaining security, visibility, and compliance, it offers one of the most robust and scalable options available.
Gong
Best for: Sales teams, revenue operations, customer success
Category: AI executive agent / AI digital agent for business
Gong is a revenue intelligence platform that applies AI to analyze sales calls, meetings, and customer interactions. Its core function is to help sales teams understand deal health, improve rep performance, and increase forecast accuracy based on real activity data.
Gong monitors pipeline activity, extracts conversational insights, and flags risks or opportunities across accounts, giving sales teams the kind of visibility that's difficult to capture manually.
Key strengths:
- Deep specialization in sales workflows
- High-quality summaries and risk detection
- Reduces manual CRM input and blind spots
- Native integrations with Salesforce, HubSpot, Slack, and email platforms
Limitations:
- Expensive for small sales teams
- Lack of transparent pricing without contacting Gong's sales team
- Requires broad adoption to unlock full value
If your organization needs clearer deal visibility, automated coaching, and more accurate forecasting based on real buyer behavior, Gong offers a mature, operationally proven solution.
CrewAI
Best for: Developers building AI multi-agent systems
Category: Multi-agent orchestration framework / AI agent platform
CrewAI is an open-source, Python-based framework for building autonomous AI agents that collaborate on complex tasks. It's designed for technical teams who want full control over multi-agent workflows.
Each "crew" of agents consists of:
- Specialized agents with distinct roles (e.g., researcher, coder, strategist)
- A central orchestrator for multi-agent workflows (like a project manager or executive agent)
- Tool integrations (APIs, vector stores, files, search, code execution)
- Memory and reasoning modules for iterative workflows
Designed for reliability and scale, CrewAI helps you avoid "black-box" agent behavior. The Python-based architecture gives you control over how agents communicate and execute tasks.
Key strengths:
- Highly flexible and developer-friendly
- Enables agent specialization and division of labor
- Works with any LLM and integrates with LangChain tools like LangGraph (a framework for defining and managing multi-agent systems)
- Community-driven, open-source, fast-evolving
Limitations:
- Requires Python fluency and understanding of multi-agent systems
- Governance and security depend on your implementation
- Not ideal for non-technical users
CrewAI is a framework for agent development rather than a finished product, but in the right hands, it's one of the most powerful tools available.
Types of AI agents for different business needs
AI agents vary widely in how they're built, what they're for, and how they operate. In a business context, most fall into one of four categories: from general-purpose AI agents to orchestrated, multi-agent platforms.
General-purpose AI agents
These are flexible agents built around large language models (LLMs), designed for open-ended tasks and often accessible through web interfaces or APIs. Examples include ChatGPT, Claude, and Perplexity.
They're useful for individual contributors and teams working on knowledge work, but their value depends heavily on prompt quality, context, and the absence of real-time integration with tools or data.
Use cases: Research, summarization, drafting content, answering complex knowledge queries
AI virtual agents for productivity
These agents focus on managing time, tasks, and attention. They integrate directly with calendars, task managers, or messaging apps to automate planning, protect focus time, or handle meeting logistics.
Examples include AI calendar agents, AI schedule makers, and task management assistants integrated with tools like Google Calendar, Outlook, or Notion.
Use cases: Scheduling meetings or tasks, time blocking, prioritization
AI coding agents
AI coding agents assist in software development by generating, editing, and reasoning through code. They operate in IDEs or agentic development platforms and often combine LLMs with code-specific tools like terminals, debuggers, and linters.
Examples include Devin, GitHub Copilot X, and Claude Code.
Use cases: Writing and editing code, debugging, refactoring, infrastructure-as-code generation, enforcing security or policy rules
Enterprise AI agent platforms
This is the most advanced tier – multi-agent platforms that combine AI agents with different specialties to achieve certain goals. They also enforce governance, assign tools and permissions, and provide logs and controls.
Organizations across industries are using such multi-agent workflows to automate cross-functional processes, enhance collaboration across silos, and improve decision-making.
Examples include CrewAI, nexos.ai, Taskade
Use cases: Cross-functional task automation, secure assistant deployment at scale, building reusable AI agent workflows with real tool access
