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Mistral launches API for building AI agents that run Python, generate images, perform RAG and more

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More The well-funded and innovative French AI startup Mistral AI is introducing a new service for enterprise customers and independent software developers alike. Mistral’s Agents application programming interface (API) allows third-party software developers to easily and rapidly […]

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The well-funded and innovative French AI startup Mistral AI is introducing a new service for enterprise customers and independent software developers alike.

Mistral’s Agents application programming interface (API) allows third-party software developers to easily and rapidly add autonomous generative AI capabilities — such as pulling information securely from enterprise documents — to their existing enterprise and independent applications using the newest Mistral proprietary model, Medium 3, as the “brains” of each agent.

It’s essentially designed to be a “plug and play” platform, with nearly limitless customization, for getting AI agents up and running to handle enterprise and developer workflows.

“With Agents API, we empower enterprises to use AI in more practical and impactful ways,” wrote Mistral’s Head of Developer Relations, Sophia Yang, PhD, on X.

Designed to complement Mistral’s existing Chat Completion API, this latest release focuses on agentic orchestration, built-in connectors, persistent memory, and the flexibility to coordinate multiple AI agents to tackle complex tasks.

Surpassing the limits of typical LLMs…

While traditional language models excel at generating text, they often fall short in executing actions or maintaining conversational context over time.

Mistral’s Agents API addresses these limitations by providing developers with the tools to create AI agents capable of performing real-world tasks, managing interactions across conversations, and dynamically orchestrating multiple agents when needed.

The Agents API comes equipped with several built-in connectors, including:

  • Code Execution: Securely runs Python code, enabling applications in data visualization, scientific computing, and other technical tasks.
  • Image Generation: Leverages Black Forest Lab FLUX1.1 [pro] Ultra to create custom visuals for marketing, education, or artistic uses.
  • Document Library: Accesses documents stored in Mistral Cloud, enhancing retrieval-augmented generation (RAG) features.
  • Web Search: Allows agents to retrieve up-to-date information from online sources, news outlets, and other reputable platforms.

The API also supports MCP tools, which connect agents to external resources like APIs, databases, user data, and documents—extending the agents’ abilities to handle dynamic, real-world content.

One significant feature of the Agents API is the integration of web search as a connector, which notably improves performance on tasks requiring accurate, up-to-date information.

In benchmark testing on the SimpleQA dataset, Mistral Large’s accuracy rose from 23% to 75% when web search was enabled. Mistral Medium showed a similar improvement, increasing from 22.08% to 82.32%.

Real-world use cases

Mistral AI has highlighted a range of use cases for the Agents API, demonstrating its flexibility across multiple sectors:

  • Coding Assistant with GitHub: An agent oversees a developer assistant powered by DevStral, managing tasks and automating code development workflows.
  • Linear Tickets Assistant: Transforms call transcripts into project deliverables using multi-server MCP architecture.
  • Financial Analyst: Sources financial metrics and securely compiles reports through orchestrated MCP servers.
  • Travel Assistant: Helps users plan trips, book accommodations, and manage travel needs.
  • Nutrition Assistant: Supports users in setting dietary goals, logging meals, and receiving personalized recommendations.

Managing context and conversations

The Agents API’s stateful conversation system ensures that agents maintain context throughout their interactions. Developers can start new conversations or continue existing ones without losing the thread, with conversation history stored and accessible for future use.

Additionally, the API supports streaming output, enabling real-time updates in response to user requests or agent actions.

Dynamic orchestration of multiple agents

A core capability of the Agents API is its ability to coordinate multiple agents seamlessly. Developers can create customized workflows, assigning specific tasks to specialized agents and enabling handoffs as needed. This modular approach allows enterprises to deploy AI agents that work together to solve complex problems more effectively.

What the Mistral Agents API means for enterprise technical decision-makers

For professionals like the Lead AI Engineer or Senior AI Engineer, the Mistral Agents API represents a powerful addition to their AI toolkit.

The ability to dynamically orchestrate agents and seamlessly integrate real-world data sources means these roles can deploy AI solutions faster and with greater precision—critical in environments where quick iteration and performance tuning are paramount.

Specifically, these professionals often balance tight deployment timelines and the need to maintain model performance across different environments.

The Agents API’s built-in connectors—like web search, document libraries, and secure code execution—can significantly reduce the need for ad hoc integrations and patchwork tooling. This streamlined approach saves time and lowers friction, allowing teams to focus more on fine-tuning models and less on building surrounding infrastructure.

Moreover, stateful conversation management and real-time updates through streaming output align well with the demands of AI orchestration and deployment. These features make it easier for engineers to maintain context across iterations and ensure consistent, high-quality interactions with end users.

For those responsible for introducing and integrating new AI tools into organizational workflows, the MCP tool support also ensures that agents can access data from a wide range of APIs and systems, further enhancing operational efficiency.

Continuing to bolster Mistral’s enterprise AI push

The release of the Agents API follows Mistral AI’s recent launch of Le Chat Enterprise, a unified AI assistant platform designed for enterprise productivity and data privacy. Le Chat Enterprise is powered by the new Mistral Medium 3 model, which offers impressive performance at a lower computational cost than larger models.

Mistral Medium 3 is particularly strong in software development tasks, outperforming comparable models in key coding benchmarks like HumanEval and MultiPL-E. It also shows competitive performance in multilingual and multimodal scenarios, making it an attractive option for businesses operating in diverse environments.

Le Chat Enterprise supports enterprise-grade features such as data sovereignty, hybrid deployment, and strict access controls, which can be crucial for organizations in regulated sectors. The platform consolidates AI functionality within a single environment, enabling customization, seamless integration with existing workflows, and full control over deployment and data security.

But it’s another proprietary service

Mistral’s earlier releases, like Mistral 7B, were open source and widely embraced by the developer community for their transparency and flexibility.

However, Mistral Medium 3 is a proprietary model—requiring access through Mistral’s platform, APIs, or partners—and is no longer available under an open-source license.

This shift has caused some frustration in the AI community, where open access and transparency are highly valued for experimentation and customization.

The Agents API itself also follows a proprietary framework: it is not available under an open-source license and is managed exclusively by Mistral, with access available via subscription and API calls.

Pricing and availability

Pricing for the Agents API aligns with Mistral’s broader suite of models and tools:

  • Mistral Medium 3: $0.4 per million input tokens and $2 per million output tokens.
  • Web Search Connector: $30 per 1,000 calls.
  • Code Execution: $30 per 1,000 calls.
  • Image Generation: $100 per 1,000 images.
  • Premium News Access: $50 per 1,000 calls.
  • Document Library with RAG: Included in plans like Team and Enterprise, with up to 30GB per user in some tiers.
  • Custom connectors, audit logs, SAML SSO, and other enterprise features: Available in Team and Enterprise plans (pricing typically requires contacting Mistral’s sales team).

For developers and enterprise customers, these costs can add up quickly—making budget considerations and careful integration planning essential.

Looking ahead

Mistral AI positions its Agents API as the backbone of enterprise-grade agentic platforms, empowering developers to create solutions that move beyond traditional text generation.

Despite the community debate around open source versus proprietary access, Mistral’s focus on enterprise-grade features, customizable workflows, and secure integrations positions this API as a significant option for businesses seeking advanced AI capabilities.

For developers and technical decision makers, the question will be whether the proprietary nature of the Agents API and the underlying models aligns with their own operational and budgetary needs. For those who prioritize rapid deployment, managed services, and full integration with enterprise systems, Mistral’s evolving platform could offer significant advantages.

For more information or to get started, Mistral AI encourages developers to explore the provided documentation and demos.

Let me know if you’d like to expand on the open-source versus proprietary discussion even more, or if you’d like to highlight another perspective!

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New Intel Xeon 6 CPUs unveiled; one powers rival Nvidia’s DGX B300

He added that his read is that “Intel recognizes that Nvidia is far and away the leader in the market for AI GPUs and is seeking to hitch itself to that wagon.” Roberts said, “basically, Intel, which has struggled tremendously and has turned over its CEO amidst a stock slide, needs to refocus to where it thinks it can win. That’s not competing directly with Nvidia but trying to use this partnership to re-secure its foothold in the data center and squeeze out rivals like AMD for the data center x86 market. In other words, I see this announcement as confirmation that Intel is looking to regroup, and pick fights it thinks it can win. “ He also predicted, “we can expect competition to heat up in this space as Intel takes on AMD’s Epyc lineup in a push to simplify and get back to basics.” Matt Kimball, vice president and principal analyst, who focuses on datacenter compute and storage at Moor Insights & Strategy, had a much different view about the announcement. The selection of the Intel sixth generation Xeon CPU, the 6776P, to support Nvidia’s DGX B300 is, he said, “important, as it validates Intel as a strong choice for the AI market. In the big picture, this isn’t about volumes or revenue, rather it’s about validating a strategy Intel has had for the last couple of generations — delivering accelerated performance across critical workloads.”  Kimball said that, In particular, there are a “couple things that I would think helped make Xeon the chosen CPU.”

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Microsoft will invest $80B in AI data centers in fiscal 2025

And Microsoft isn’t the only one that is ramping up its investments into AI-enabled data centers. Rival cloud service providers are all investing in either upgrading or opening new data centers to capture a larger chunk of business from developers and users of large language models (LLMs).  In a report published in October 2024, Bloomberg Intelligence estimated that demand for generative AI would push Microsoft, AWS, Google, Oracle, Meta, and Apple would between them devote $200 billion to capex in 2025, up from $110 billion in 2023. Microsoft is one of the biggest spenders, followed closely by Google and AWS, Bloomberg Intelligence said. Its estimate of Microsoft’s capital spending on AI, at $62.4 billion for calendar 2025, is lower than Smith’s claim that the company will invest $80 billion in the fiscal year to June 30, 2025. Both figures, though, are way higher than Microsoft’s 2020 capital expenditure of “just” $17.6 billion. The majority of the increased spending is tied to cloud services and the expansion of AI infrastructure needed to provide compute capacity for OpenAI workloads. Separately, last October Amazon CEO Andy Jassy said his company planned total capex spend of $75 billion in 2024 and even more in 2025, with much of it going to AWS, its cloud computing division.

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John Deere unveils more autonomous farm machines to address skill labor shortage

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OpenAI’s red teaming innovations define new essentials for security leaders in the AI era

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI has taken a more aggressive approach to red teaming than its AI competitors, demonstrating its security teams’ advanced capabilities in two areas: multi-step reinforcement and external red teaming. OpenAI recently released two papers that set a new competitive standard for improving the quality, reliability and safety of AI models in these two techniques and more. The first paper, “OpenAI’s Approach to External Red Teaming for AI Models and Systems,” reports that specialized teams outside the company have proven effective in uncovering vulnerabilities that might otherwise have made it into a released model because in-house testing techniques may have missed them. In the second paper, “Diverse and Effective Red Teaming with Auto-Generated Rewards and Multi-Step Reinforcement Learning,” OpenAI introduces an automated framework that relies on iterative reinforcement learning to generate a broad spectrum of novel, wide-ranging attacks. Going all-in on red teaming pays practical, competitive dividends It’s encouraging to see competitive intensity in red teaming growing among AI companies. When Anthropic released its AI red team guidelines in June of last year, it joined AI providers including Google, Microsoft, Nvidia, OpenAI, and even the U.S.’s National Institute of Standards and Technology (NIST), which all had released red teaming frameworks. Investing heavily in red teaming yields tangible benefits for security leaders in any organization. OpenAI’s paper on external red teaming provides a detailed analysis of how the company strives to create specialized external teams that include cybersecurity and subject matter experts. The goal is to see if knowledgeable external teams can defeat models’ security perimeters and find gaps in their security, biases and controls that prompt-based testing couldn’t find. What makes OpenAI’s recent papers noteworthy is how well they define using human-in-the-middle

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