As 10 melhores plataformas de agentes de IA em 2026

Avaliações de plataformas de agentes de IA

1. 

   1

   DuploCloud

   Best for guarded DevOps infrastructure agents

   • Free demo available
   • From $3,500/month

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   DuploCloud is an agentic DevOps platform that combines AI-powered infrastructure automation, Kubernetes and multi-cloud management, CI/CD orchestration, and built-in security and compliance workflows.

   Who Is DuploCloud Best For?

   DuploCloud is a strong fit for DevOps and platform engineering teams that need AI-driven infrastructure automation across Kubernetes and multi-cloud environments with built-in compliance guardrails.

   Why I Picked DuploCloud

   I picked DuploCloud as one of the best because its guardrails aren't bolted on as an afterthought—they're built into every layer of how agents act. Every infrastructure change requires human approval before execution, MCP tool calls gate individually with no "always allow" bypass, and shell commands run against approve/reject patterns so nothing slips through unchecked. I especially like that the built-in DevOps agent handles the full Kubernetes and cloud lifecycle, from provisioning to drift detection and security remediation, within that same governed framework.

   DuploCloud Key Features

   • Multi-cloud infrastructure agent: A built-in DevOps agent provisions and manages resources across AWS, GCP, and Azure, including VPCs, Kubernetes clusters, RDS, and load balancers.
   • Workspace-scoped memory and knowledge base: Each workspace maintains persistent context across tickets, with support for ingesting PDF, Office, HTML, and text files up to 2 GB for agent search during conversations.
   • Model router: Routes agent execution across Claude, OpenAI, Gemini, and open-source models, with model selection configurable at the project, workspace, or platform level.
   • Token and spend quota controls: Administrators can set USD-denominated spend limits at the workspace, project, ticket, user, or platform scope to cap LLM costs.

   DuploCloud Integrations

   DuploCloud integrates with GitHub, GitLab, CircleCI, Terraform, Kubernetes, AWS, Google Cloud, Microsoft Azure, Datadog, and Prometheus. It supports REST APIs, MCP connections, and workflows through Jira and Slack.

   Pros and Cons

   Pros:

   • Built-in compliance controls support safer operations
   • Human approvals gate infrastructure changes
   • Self-service cuts infrastructure wait times

   Cons:

   • Cloud-native feature updates can lag
   • No documented autonomous agent-to-agent delegation
   Learn more about DuploCloud:

   • Check out DuploCloud on their website
2. 

   2

   UiPath

   Best for RPA-plus-agent automation in enterprise

   • Free plan available
   • From $25/month

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   Customer Rating:4.6/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   UiPath is an agentic automation platform that combines RPA robots, multi-agent orchestration, LLM integration, and human-in-the-loop controls to automate complex, multi-step enterprise workflows.

   Who Is UiPath Best For?

   UiPath is a strong fit for enterprise IT and automation engineering teams managing complex, multi-system workflows that mix legacy infrastructure with modern AI agents.

   Why I Picked UiPath

   UiPath earns its spot on my shortlist because no other platform in this category converges RPA robots and AI agents the way it does. I love that Maestro orchestrates agents, robots, and human approvals within a single BPMN workflow, so your team can automate across both modern APIs and legacy systems that have no APIs at all. Agent Builder lets you configure multi-agent logic visually or in code, while the AI Trust Layer governs every LLM call across providers like Azure OpenAI, Anthropic, and AWS Bedrock from one centralized policy layer.

   UiPath Key Features

   • Context Grounding: Retrieves information from permissioned, access-controlled knowledge bases using vector databases like FAISS and Pinecone for RAG-style workflows.
   • Action Center HITL controls: Pauses agent execution at defined checkpoints so human reviewers can approve, reject, or redirect decisions before high-risk actions proceed.
   • MCP-native tool integration: Connects agents to external systems through four MCP integration types in Orchestrator, covering pre-built, coded, command-based, and remote server options.
   • Maestro Case Management: Models complex, adaptive workflows with stages, SLA tracking, escalation rules, and exception handling across agents, robots, and human participants.

   UiPath Integrations

   UiPath offers native integrations with Azure OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Snowflake, Databricks, Salesforce, and CyberArk. An API is available for custom integrations.

   Pros and Cons

   Pros:

   • Integrates agentic automation with legacy systems
   • Advanced audit trails and governance features
   • Orchestrates agents, robots, and humans together

   Cons:

   • Limited independent container deployment
   • Needs significant hardware resources
   Learn more about UiPath:

   • Check out UiPath on their website
3. 

   3

   n8n

   Best for self-hosted multi-agent automation

   • Free trial available
   • From €20/month (billed annually)

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   Customer Rating:4.7/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   n8n is an open-source AI agent platform that combines a visual workflow canvas with embedded JavaScript and Python support to build, orchestrate, and deploy single-agent and multi-agent automation pipelines.

   Who Is n8n Best For?

   n8n is a strong fit for DevOps and IT engineering teams that need full control over their infrastructure and want to run AI agent workflows on their own servers.

   Why I Picked n8n

   I've included n8n in my top picks because it's the only tool on this list where you can run the entire agent runtime on your own infrastructure, including air-gapped environments, with no data leaving your network. I particularly like the supervisor/worker multi-agent architecture, which lets you build hierarchical pipelines where a coordinating agent delegates tasks to specialized sub-agents for research, QA, or execution. The built-in human-in-the-loop approval gates are also a genuine differentiator, letting you embed manual checkpoints before any high-risk agent action runs.

   n8n Key Features

   • Model-agnostic LLM routing: Connect multiple LLM providers—OpenAI, Anthropic, Gemini, and self-hosted models—within a single workflow and route tasks to different models based on logic you define.
   • AI Evaluations framework: Run pre-deployment regression tests on agent workflows using real data to track correctness, token usage, and response quality before pushing changes to production.
   • Git-based version control: Manage workflow definitions as code with full version history, staging and production environment separation, and side-by-side workflow diffs for auditable change management.
   • Vector database integration: Connect natively to Pinecone, Qdrant, Weaviate, and other vector stores to build RAG pipelines that let agents query internal knowledge bases, documents, and wikis.

   n8n Integrations

   n8n offers 500+ native integration nodes, including OpenAI, Anthropic, Google Gemini, Pinecone, Qdrant, Weaviate, Salesforce, HubSpot, Slack, and GitHub. An API is available for custom integrations.

   Pros and Cons

   Pros:

   • Manages vector database queries
   • Commits workflow versions directly
   • Fully self-hosted in air-gapped environments

   Cons:

   • Workflow editor lacks auto-save functionality
   • Visual canvas gets cluttered on large workflows
   Learn more about n8n:

   • Check out n8n on their website
4. 

   4

   Kore.ai

   Best for enterprise multi-agent IT automation

   • Free plan available
   • Pricing upon request

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   Customer Rating:4.6/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   Kore.ai is an enterprise AI agent platform that lets you build, orchestrate, and govern multi-agent systems using a visual studio, a compiled declarative agent language, and an AI-generated agent architect.

   Who Is Kore.ai Best For?

   Kore.ai is a strong fit for large enterprises in regulated industries—banking, healthcare, insurance, and government—that need to deploy multi-agent IT automation with strict compliance and governance requirements.

   Why I Picked Kore.ai

   I picked Kore.ai as one of the best because it's built specifically for the kind of multi-agent IT automation that large enterprises actually need—think coordinating ServiceNow ticket resolution, BMC Helix alerts, and Jira updates across a single workflow without manual handoffs. I'm particularly impressed by its six natively compiled orchestration patterns, which let you run supervisor, fan-out, and delegation logic as first-class constructs rather than workarounds. The engine-enforced guardrails and FedRAMP Moderate authorization make it the right call for regulated environments where governance isn't optional.

   Kore.ai Key Features

   • Agent Blueprint Language (ABL): A compiled, declarative DSL that lets you define agent goals, tools, memory, and guardrail logic as Git-native YAML artifacts with full CI/CD pipeline support.
   • Model Hub: A model-agnostic routing layer that supports OpenAI, Anthropic, Cohere, NVIDIA, Meta LLaMA 2, Hugging Face open-source models, and bring-your-own-model configurations.
   • Auto Loop™: A continuous optimization engine that monitors production agent outcomes and auto-tunes prompts, tools, and workflows without manual re-engineering cycles.
   • Agent Evals: An automated testing framework that runs multi-scenario quality and safety evaluations with regression tracking integrated directly into deployment pipelines.

   Kore.ai Integrations

   Kore.ai offers native integrations with ServiceNow, BMC Helix, Jira, Ivanti, Salesforce, SAP, Workday, ADP, Slack, and Microsoft Teams. An API is available for custom integrations.

   Pros and Cons

   Pros:

   • CI/CD-native agent versioning with full audit trails
   • Recompiles execution paths automatically
   • Agent orchestration patterns are natively compiled

   Cons:

   • Knowledge base refresh is not fully automatic
   • Steep platform engineering requirements for advanced use
   Learn more about Kore.ai:

   • Check out Kore.ai on their website
5. 

   5

   Dataiku

   Best for governing agents across the enterprise

   • 14-day free trial
   • Pricing upon request

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   Customer Rating:4.6/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   Dataiku is an enterprise AI agent platform that combines visual and code-based agent building, multi-agent orchestration, model-agnostic LLM routing, and built-in governance tools including guardrails, approval workflows, and audit trails.

   Who Is Dataiku Best For?

   Dataiku is a strong fit for large enterprises that need to build, deploy, and govern AI agents across multiple teams, platforms, and compliance requirements from a single platform.

   Why I Picked Dataiku

   Dataiku earns its spot on my shortlist because no other platform matches its depth of built-in governance across the full agent lifecycle. I particularly like the LLM Guard Services layer, which combines Cost Guard, Safe Guard, and Quality Guard to enforce budget limits, detect PII, and block prompt injection before agents ever reach production. Pair that with Dataiku Govern's approval workflows and audit trails, and you have end-to-end oversight that most agent frameworks can't replicate natively.

   Dataiku Key Features

   • LLM Mesh routing: A model-agnostic gateway that connects to 10+ LLM providers—including OpenAI, Anthropic, and Mistral—with dynamic routing, response caching, and cost tracking built in.
   • Visual and code agent builder: Dual-path agent creation lets non-technical users build agents with a no-code visual flow builder while engineers write full-code agents using the SDK.
   • ReAct loop execution: Simple Visual Agents apply a native Reason-and-Act loop, autonomously selecting tools, executing actions, observing results, and iterating until task completion.
   • Agent Run Inspector: A step-by-step debugging interface that surfaces warnings and errors at each stage of an agent's execution for diagnosing unexpected behaviour.

   Dataiku Integrations

   Dataiku offers native integrations with OpenAI, Anthropic, Mistral, AWS Bedrock, Azure OpenAI, Databricks, Snowflake Cortex, Salesforce, ServiceNow, and Jira, plus Slack and Microsoft Teams for agent deployment. It also supports Model Context Protocol (MCP) servers and OpenAPI for custom tool and API connections.

   Pros and Cons

   Pros:

   • Centralized observability for multi-platform agents
   • Built-in LLM guardrails and approval workflows
   • Visual and code agents in one platform

   Cons:

   • UI can be clunky for complex workflows
   • Agent management module not yet available
   Learn more about Dataiku:

   • Check out Dataiku on their website
   • Read my in-depth review of Dataiku
6. 

   6

   LangChain LangGraph

   Best for graph-based multi-agent orchestration

   • Free plan + free trial + free demo available
   • From $39/user/month

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   Customer Rating:4.4/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   LangChain LangGraph is a graph-based AI agent framework that supports stateful, multi-step agent workflows, multi-agent orchestration, human-in-the-loop controls, and long-term memory management across Python and JavaScript/TypeScript environments.

   Who Is LangChain LangGraph Best For?

   LangChain LangGraph is a strong fit for engineering teams building complex, stateful AI agents that require fine-grained control over multi-step workflows and multi-agent coordination.

   Why I Picked LangChain LangGraph

   LangChain LangGraph earns its spot on my shortlist because its StateGraph model gives you explicit, node-by-node control over how agents think, branch, retry, and loop—something linear chain-based frameworks simply can't replicate. I particularly like its supervisor/worker architecture for multi-agent orchestration, where a top-level agent decomposes tasks and delegates to specialized subagents running in parallel. Built-in checkpointing at every graph node means long-running agents can pause for human approval, resume after a failure, or rewind to any prior state for debugging.

   LangChain LangGraph Key Features

   • LangSmith tracing: Captures distributed traces of every agent step, LLM call, and tool invocation so you can inspect exactly what happened inside a running agent.
   • Human-in-the-loop interrupt gates: Pauses agent execution at defined graph nodes to request human approval or input before proceeding to the next step.
   • Long-term memory store: Persists agent state and conversation context across sessions using a built-in memory store with semantic search support.
   • LangGraph Studio: A visual IDE for inspecting, debugging, and iterating on agent execution graphs in real time during development.

   LangChain LangGraph Integrations

   LangChain LangGraph offers native integrations with OpenAI, Anthropic, Google Gemini, Mistral, Pinecone, Weaviate, Chroma, pgvector, PostgreSQL, and Redis. An API is available for custom integrations.

   Pros and Cons

   Pros:

   • First-class human-in-the-loop interrupt gates
   • Stores semantic historical interactions
   • Fine-grained control over agent workflows

   Cons:

   • High scaling can trigger memory issues
   • Predefined static graph state design is required
   Learn more about LangChain LangGraph:

   • Check out LangChain LangGraph on their website
7. 

   7

   Relevance AI

   Best for no-code multi-agent workforce builds

   • Free demo available
   • Pricing upon request

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   Customer Rating:4.3/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   Relevance AI is an AI agent platform built around a visual multi-agent canvas, letting you design, deploy, and govern autonomous agent workforces with built-in LLM orchestration, a 2,700+ integration library, hybrid RAG memory, and a native eval and observability system.

   Who Is Relevance AI Best For?

   Relevance AI is a strong fit for operations and automation teams that need to deploy multi-agent workflows without writing infrastructure code from scratch.

   Why I Picked Relevance AI

   I picked Relevance AI as one of the best because the visual Workflows canvas lets you assemble entire multi-agent pipelines without writing a line of infrastructure code. You can connect specialist agents with defined handoffs, parallel execution streams, and built-in approval gates, all from a drag-and-drop interface. I also love that every agent ships with an auto-generated eval suite that scores live production runs and blocks failing versions from deploying.

   Relevance AI Key Features

   • Hybrid RAG memory: Connect agents to internal knowledge bases with semantic search, auto-syncing with Google Drive, SharePoint, Notion, and Confluence.
   • Sandboxed code execution: Run Python and JavaScript natively within agent tool steps in an isolated sandbox environment.
   • LLM routing: Route tasks across multiple providers—including Anthropic, OpenAI, and Google—with fallback models and per-task cost-optimized model selection.
   • Human-in-the-loop approval gates: Pause agent execution at configurable checkpoints for human sign-off before high-stakes actions proceed.

   Relevance AI Integrations

   Relevance AI offers native integrations with Salesforce, HubSpot, Slack, Google Drive, SharePoint, Notion, Confluence, GitHub, Snowflake, and DataDog. An API is available for custom integrations.

   Pros and Cons

   Pros:

   • Embeds secure script execution spaces
   • Integrated evaluation gates and agent versioning
   • Visual workforce canvas for multi-agent workflows

   Cons:

   • No self-hosted or on-premises deployment option
   • GTM and sales templates dominate use cases
   Learn more about Relevance AI:

   • Check out Relevance AI on their website
8. 

   8

   CrewAI

   Best for Python-native multi-agent collaboration

   • Free plan + free trial available
   • Pricing upon request

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   Customer Rating:4.2/5This rating combines scores from multiple user review sites to reflect overall customer sentiment about the product.

   

   CrewAI is a Python-native AI agent platform that lets you build, orchestrate, and deploy multi-agent systems using a code-first SDK, a visual studio editor, and a suite of tools for memory management, LLM routing, and workflow automation.

   Who Is CrewAI Best For?

   CrewAI is a strong fit for engineering teams that need to build and govern multi-agent AI systems in Python, particularly in organizations with compliance or security requirements.

   Why I Picked CrewAI

   I picked CrewAI as one of the best because it's built Python-first, which means agent definitions, tool bindings, and orchestration logic all live in code your team already versions and reviews. I especially like how it separates Crews (autonomous, role-based agents that delegate tasks between themselves) from Flows (deterministic, event-driven pipelines with conditional branching and typed state), letting you blend both patterns in a single runtime depending on how much autonomy a given workflow needs.

   CrewAI Key Features

   • LLM routing per agent: Assign a different LLM to each agent in a crew, including a separate model dedicated specifically to tool/function calls.
   • Human-in-the-loop gates: The @human_feedback decorator pauses flow execution at defined checkpoints so a human can review, override, or approve before the next step runs.
   • Enterprise connector repository: Pre-built connectors for Salesforce, Jira, Snowflake, Microsoft 365, Google Workspace, Slack, and Zendesk pipe real system data directly into agent workflows.
   • PII redaction at runtime: A governance hook automatically strips personally identifiable information at every LLM and tool call before data leaves your environment.

   CrewAI Integrations

   CrewAI offers native integrations with Salesforce, HubSpot, Snowflake, Jira, Zendesk, Slack, Gmail, Microsoft 365, Teams, and Google Workspace. An API is available for custom integrations.

   Pros and Cons

   Pros:

   • Per-agent LLM assignment for advanced orchestration
   • Hierarchical memory structure with composite scoring
   • Native Python SDK with code and visual builder

   Cons:

   • Complex automation setup requires strong Python skills
   • Consumes high local memory
   Learn more about CrewAI:

   • Check out CrewAI on their website
9. 

   9

   Microsoft Foundry

   Best for Microsoft-native multi-agent deployments

   • 30-day free trial available
   • From $0.165 per 1M input tokens

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   Microsoft Foundry is an AI agent platform built on Azure that lets you build, deploy, and orchestrate agents using a 10,000+ model catalogue, multi-agent workflows, and native integrations across the Microsoft ecosystem.

   Who Is Microsoft Foundry Best For?

   Microsoft Foundry is the right fit for enterprise IT and engineering teams already running on Azure who need to build and orchestrate multi-agent AI workflows across the Microsoft ecosystem.

   Why I Picked Microsoft Foundry

   Microsoft Foundry earns its spot on my shortlist because of how deeply it's wired into the Azure ecosystem for multi-agent deployments. I love that Connected Agents lets one agent call another as a specialized sub-agent, so you can decompose something like an IT incident response workflow into discrete agents handling detection, diagnosis, and remediation in parallel. The Agent Harness adds planning and to-do tracking on top of that, keeping long-running autonomous runs on course without constant human intervention.

   Microsoft Foundry Key Features

   • Model benchmark leaderboard: Compare 10,000+ models side by side on quality, latency, throughput, and cost before committing to a deployment.
   • PyRIT red teaming: Run automated adversarial simulations against your agents to surface safety and security vulnerabilities before going to production.
   • Provisioned Throughput Units (PTUs): Reserve dedicated model processing capacity on a monthly or annual basis for predictable latency on high-volume workloads.
   • Per-agent Entra identity: Each hosted agent is automatically assigned its own Microsoft Entra identity, giving you granular access control at the individual agent level.

   Microsoft Foundry Integrations

   Microsoft Foundry offers native integrations with Microsoft 365, Teams, SharePoint, Fabric, Entra, Cosmos DB, Key Vault, Azure Functions, and GitHub. An API is available for custom integrations.

   Pros and Cons

   Pros:

   • Per-agent Entra identity enables granular access
   • MSC/LLM model benchmark leaderboard built in
   • Integrated multi-agent orchestration with A2A protocol

   Cons:

   • Needs proprietary cloud infrastructure
   • No built-in disaster recovery for agent state
   Learn more about Microsoft Foundry:

   • Check out Microsoft Foundry on their website
10. 

    10

    Agent Development Kit (ADK)

    Best for multi-agent orchestration on Google Cloud

    • Free demo available
    • Pricing upon request

    Visit Website

    

    Google ADK is an open-source, code-first AI agent framework with multi-language SDKs (Python, TypeScript, Go, Java, and Kotlin) for building, evaluating, and deploying single agents, multi-agent pipelines, and graph-based workflows with built-in support for MCP, A2A protocol, and sandboxed code execution.

    Who Is Agent Development Kit (ADK) Best For?

    ADK is a strong fit for engineering teams already running infrastructure on Google Cloud who need to build and orchestrate production-grade multi-agent systems at scale.

    Why I Picked Agent Development Kit (ADK)

    ADK earns its spot on my shortlist because of how naturally it handles multi-agent orchestration on Google Cloud infrastructure. I love that ADK 2.0 introduced graph-based and collaborative workflow patterns, where a coordinator agent dynamically delegates tasks to specialized sub-agents, letting teams decompose complex IT and DevOps pipelines across purpose-built agents. Per-agent model assignment means each sub-agent runs the right model for its task, and native A2A protocol support lets those agents talk across frameworks without custom plumbing.

    Agent Development Kit (ADK) Key Features

    • Multi-language SDK support: Build agents in Python, TypeScript, Go, Java, or Kotlin using consistent APIs across all five languages.
    • ADK Eval framework: Run test files, evalset, and conformance evaluations with LLM-as-judge scoring, hallucination detection, and automated user simulation for production agent reliability testing.
    • Human-in-the-loop controls: Pause and resume agent execution mid-workflow using RequestedInput events and tool confirmation flows before high-risk actions execute.
    • Sandboxed code execution: Run agent-generated code and shell commands in isolated server-side sandboxes via the Code Execution Tool, Daytona integration, or Bash Tool.

    Agent Development Kit (ADK) Integrations

    Agent Development Kit offers native integrations with Gemini, Claude, BigQuery, Firestore, Datadog, Jira, Confluence, Cloud Run, and GKE. An API is available for custom integrations.

    Pros and Cons

    Pros:

    • Native support for open agent communication protocols
    • Interprets local shell commands safely
    • Multi-language SDKs for Python, Go, Java, TypeScript, Kotlin

    Cons:

    • Most production deployments remain Google Cloud-only
    • State management in multi-turn agents is complex
    Learn more about Agent Development Kit (ADK):

    • Check out Agent Development Kit (ADK) on their website