THE POINT / WHAT CHANGES FOR YOU

From news to practice.

To choose a platform, compare integrations, controls, and required skills. A developer framework and a corporate service address different needs.

What "best" means in the agents market

There is no universal ranking of the best AI agent makers. A no-code environment, a developer framework and a customer service platform meet different needs. This global desk selection considers ten companies with a public and documented offering to build, deploy or manage agents. It does not measure market shares or comparative performance; the order of the cards does not indicate a ranking.

Three levels often confused

The first level is the model, which interprets requests and produces decisions or content. The second is orchestration, which organises tools, steps and work state. The third is the operational application, connected to users, data and processes. Some companies cover multiple levels. Comparing them requires clarifying whether you are looking for a ready product, a configurable environment or components to develop a solution.

1. OpenAI — Agents SDK

OpenAI offers an SDK to build agents with instructions, tools, handover steps and controls. The documentation includes execution tracing and human involvement. It is a proposal to consider when a technical team wants to program behaviour and observe steps. The chosen model is distinct from the application structure: the final product also depends on data, tools and permissions.

Discover OpenAI’s official offering ↗

2. Anthropic — Claude Agent SDK

Anthropic develops Claude and provides tools to create agentic applications. Its documentation distinguishes predefined paths and systems where the model decides how to proceed. It is a reference for those seeking a technical approach to agent construction and wishing to evaluate the balance of autonomy, quality and complexity. The choice requires testing on the specific task.

Discover Anthropic’s official offering ↗

3. Google — Gemini platform

Google Cloud offers a platform to build, deploy and govern enterprise agents connected to organisational data. The offering combines development and operational management. It is to be considered when the project requires cloud infrastructure and integration with applications and repositories. Service availability, required skills and data access modes remain criteria to verify in the concrete setup.

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4. Microsoft — Copilot Studio

Copilot Studio is Microsoft’s platform to create and manage agents, workflows and applications with no-code tools. Its role is to make agent building accessible also through a configurable interface. It is a relevant offering for organisations considering automation within their Microsoft environment, verifying connectors, licences, permissions and lifecycle management.

Discover Microsoft’s official offering ↗

5. Amazon Web Services — Amazon Bedrock Agents

AWS offers Amazon Bedrock Agents within its cloud services ecosystem. The proposal addresses those building applications with models and tools connected to business processes. Evaluation must consider the overall architecture: identity, data sources, integrations and operating costs. A managed platform can simplify some activities but does not replace control design.

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6. Salesforce — Agentforce

Salesforce proposes Agentforce as an agent platform, with particular attention to customer support and service processes. The point to observe is the connection between conversation, case information and operational actions. For a company, testing must verify which requests the agent actually resolves, when it escalates the case to a human and which data it is authorised to use.

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7. ServiceNow — AI Agents and AI Agent Studio

ServiceNow places agents in workflows of IT, customer support and human resources. AI Agent Studio allows building and customising agents, while the platform offers coordination and control tools. It is a proposal to consider for processes already organised into requests, cases and approvals. Integration with existing processes matters as much as model capabilities.

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8. IBM — watsonx Orchestrate

IBM presents watsonx Orchestrate as a platform to build, deploy, coordinate and govern agents. The distinctive theme is managing a set of agents: roles, activities, dependencies and costs. It is a proposal to watch when the organisation must bring order to multiple initiatives, verifying which components can be integrated and how operational rules are applied.

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9. NVIDIA — Models and components for agentic applications

NVIDIA provides models, blueprints and infrastructures usable in agent development. Its role includes technological components on which to build a solution, even in Physical AI contexts. It should not therefore be compared as if offering only a ready-to-use assistant: the choice also concerns computing, distribution and integration of various elements.

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10. LangChain — LangGraph

LangChain develops LangGraph, a framework and runtime environment to orchestrate agents with state and complex activities. The documentation highlights flow control and the combination of deterministic steps and model-guided decisions. It is a reference for teams wanting to build custom systems, also taking on responsibilities for integration, testing and management.

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How to turn selection into a useful comparison

To choose, define a process and prepare a small set of real cases, including errors and ambiguous requests. Ask each provider to demonstrate the same results: completion, escalation to a person, traceability and compliance with authorisations. Then compare integration, data portability and total cost per completed task. Promotional figures do not replace a trial under your own organisation's conditions.

Read the guide: how to structure an AI agent and control its risks ↗

Sources and documents

Document date or reference period: Product pages and official documentation consulted on 23 October 2026.

Text prepared with AI assistance based on the indicated sources. Interpretive evaluations are distinct from data and statements from sources.

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