Jul 8, 2025
Galileo Joins MongoDB's AI Applications Program as Their First Agentic Evaluation Platform
Conor Bronsdon
Head of Developer Awareness
Conor Bronsdon
Head of Developer Awareness
The enterprise AI landscape is undergoing a fundamental shift. While most companies spent 2023 and early 2024 building proofs-of-concept with RAG systems, 2025 is shaping up to be the year organizations begin to move AI agents into production at scale.
But there’s a problem: traditional observability tools weren't designed for autonomous systems that make decisions, call tools, and operate across multi-step workflows. When an agent hallucinates or makes a costly mistake, you need more than basic logging; you need real-time evaluation and adaptive guardrails that can intervene before damage occurs. This becomes even more crucial as you build multi-agent systems.
That's why I'm excited to announce that Galileo has joined MongoDB's AI Applications Program (MAAP) as the first and only Agentic Evaluation Platform in their ecosystem!
Why This Partnership Matters
MongoDB's MAAP combines the essential components needed for production AI: data management, LLM orchestration, model hosting, and now, with Galileo, comprehensive agent reliability. This isn't just another integration; it's recognition that agentic evaluation is now table stakes for enterprise AI deployment.
The timing couldn't be more critical. Capgemini's research shows that 10% of organizations already use AI agents, with over half planning adoption in 2025. However, without proper evaluation frameworks, these deployments often fail due to unpredictable behaviors, compliance violations, or security breaches. Thanks to these challenges, Gartner is already predicting that over 40% of agentic AI projects will be cancelled by the end of 2027.
Traditional observability solutions fall short because they can't keep pace with the non-determinism and dynamic failure modes inherent in agentic systems. A single bad tool call can expose sensitive data or incur significant costs, making real-time intervention essential, not optional.
What Galileo Brings to MAAP
Our platform addresses the unique challenges of agentic reliability through:
Real-time evaluations powered by our proprietary Luna-2 small language models that catch issues before they impact users
Adaptive guardrails that learn from your specific use cases and business context
Automated root cause analysis that helps teams understand not just what went wrong but why
Multi-agent observability designed for the complex interaction patterns that define modern AI systems
This isn't about retrofitting traditional monitoring tools for AI; it's purpose-built infrastructure for autonomous systems that need to operate reliably at enterprise scale.
For MongoDB's ecosystem partners and customers, this means being able to deploy agents confidently, knowing that potential issues will be identified and addressed proactively rather than reactively.
The future of enterprise AI isn't just about building smarter agents—it's about building agents you can trust. With MongoDB's comprehensive data platform and Galileo's agentic evaluation capabilities, that future is here.
Learn More
Want to see how MongoDB and Galileo work together to enable reliable agentic systems? Join our upcoming webinar on July 17th at 12 PM ET, where our Principal Developer Advocate Roie Schwaber-Cohen and MongoDB's Staff Developer Advocate Richmond Alake will demonstrate:
The complete AI reliability framework: a systematic approach to identifying, measuring, and eliminating hallucination.
Production-Ready implementation strategies: hands-on techniques for integrating Voyage AI embeddings, MongoDB Atlas retrieval mechanisms, and Galileo evaluation pipelines into your existing AI infrastructure.
Advanced hallucination reduction techniques: proven methods for improving context relevancy through optimized retrieval strategies, reranking algorithms, and evaluation workflows.
The enterprise AI landscape is undergoing a fundamental shift. While most companies spent 2023 and early 2024 building proofs-of-concept with RAG systems, 2025 is shaping up to be the year organizations begin to move AI agents into production at scale.
But there’s a problem: traditional observability tools weren't designed for autonomous systems that make decisions, call tools, and operate across multi-step workflows. When an agent hallucinates or makes a costly mistake, you need more than basic logging; you need real-time evaluation and adaptive guardrails that can intervene before damage occurs. This becomes even more crucial as you build multi-agent systems.
That's why I'm excited to announce that Galileo has joined MongoDB's AI Applications Program (MAAP) as the first and only Agentic Evaluation Platform in their ecosystem!
Why This Partnership Matters
MongoDB's MAAP combines the essential components needed for production AI: data management, LLM orchestration, model hosting, and now, with Galileo, comprehensive agent reliability. This isn't just another integration; it's recognition that agentic evaluation is now table stakes for enterprise AI deployment.
The timing couldn't be more critical. Capgemini's research shows that 10% of organizations already use AI agents, with over half planning adoption in 2025. However, without proper evaluation frameworks, these deployments often fail due to unpredictable behaviors, compliance violations, or security breaches. Thanks to these challenges, Gartner is already predicting that over 40% of agentic AI projects will be cancelled by the end of 2027.
Traditional observability solutions fall short because they can't keep pace with the non-determinism and dynamic failure modes inherent in agentic systems. A single bad tool call can expose sensitive data or incur significant costs, making real-time intervention essential, not optional.
What Galileo Brings to MAAP
Our platform addresses the unique challenges of agentic reliability through:
Real-time evaluations powered by our proprietary Luna-2 small language models that catch issues before they impact users
Adaptive guardrails that learn from your specific use cases and business context
Automated root cause analysis that helps teams understand not just what went wrong but why
Multi-agent observability designed for the complex interaction patterns that define modern AI systems
This isn't about retrofitting traditional monitoring tools for AI; it's purpose-built infrastructure for autonomous systems that need to operate reliably at enterprise scale.
For MongoDB's ecosystem partners and customers, this means being able to deploy agents confidently, knowing that potential issues will be identified and addressed proactively rather than reactively.
The future of enterprise AI isn't just about building smarter agents—it's about building agents you can trust. With MongoDB's comprehensive data platform and Galileo's agentic evaluation capabilities, that future is here.
Learn More
Want to see how MongoDB and Galileo work together to enable reliable agentic systems? Join our upcoming webinar on July 17th at 12 PM ET, where our Principal Developer Advocate Roie Schwaber-Cohen and MongoDB's Staff Developer Advocate Richmond Alake will demonstrate:
The complete AI reliability framework: a systematic approach to identifying, measuring, and eliminating hallucination.
Production-Ready implementation strategies: hands-on techniques for integrating Voyage AI embeddings, MongoDB Atlas retrieval mechanisms, and Galileo evaluation pipelines into your existing AI infrastructure.
Advanced hallucination reduction techniques: proven methods for improving context relevancy through optimized retrieval strategies, reranking algorithms, and evaluation workflows.
The enterprise AI landscape is undergoing a fundamental shift. While most companies spent 2023 and early 2024 building proofs-of-concept with RAG systems, 2025 is shaping up to be the year organizations begin to move AI agents into production at scale.
But there’s a problem: traditional observability tools weren't designed for autonomous systems that make decisions, call tools, and operate across multi-step workflows. When an agent hallucinates or makes a costly mistake, you need more than basic logging; you need real-time evaluation and adaptive guardrails that can intervene before damage occurs. This becomes even more crucial as you build multi-agent systems.
That's why I'm excited to announce that Galileo has joined MongoDB's AI Applications Program (MAAP) as the first and only Agentic Evaluation Platform in their ecosystem!
Why This Partnership Matters
MongoDB's MAAP combines the essential components needed for production AI: data management, LLM orchestration, model hosting, and now, with Galileo, comprehensive agent reliability. This isn't just another integration; it's recognition that agentic evaluation is now table stakes for enterprise AI deployment.
The timing couldn't be more critical. Capgemini's research shows that 10% of organizations already use AI agents, with over half planning adoption in 2025. However, without proper evaluation frameworks, these deployments often fail due to unpredictable behaviors, compliance violations, or security breaches. Thanks to these challenges, Gartner is already predicting that over 40% of agentic AI projects will be cancelled by the end of 2027.
Traditional observability solutions fall short because they can't keep pace with the non-determinism and dynamic failure modes inherent in agentic systems. A single bad tool call can expose sensitive data or incur significant costs, making real-time intervention essential, not optional.
What Galileo Brings to MAAP
Our platform addresses the unique challenges of agentic reliability through:
Real-time evaluations powered by our proprietary Luna-2 small language models that catch issues before they impact users
Adaptive guardrails that learn from your specific use cases and business context
Automated root cause analysis that helps teams understand not just what went wrong but why
Multi-agent observability designed for the complex interaction patterns that define modern AI systems
This isn't about retrofitting traditional monitoring tools for AI; it's purpose-built infrastructure for autonomous systems that need to operate reliably at enterprise scale.
For MongoDB's ecosystem partners and customers, this means being able to deploy agents confidently, knowing that potential issues will be identified and addressed proactively rather than reactively.
The future of enterprise AI isn't just about building smarter agents—it's about building agents you can trust. With MongoDB's comprehensive data platform and Galileo's agentic evaluation capabilities, that future is here.
Learn More
Want to see how MongoDB and Galileo work together to enable reliable agentic systems? Join our upcoming webinar on July 17th at 12 PM ET, where our Principal Developer Advocate Roie Schwaber-Cohen and MongoDB's Staff Developer Advocate Richmond Alake will demonstrate:
The complete AI reliability framework: a systematic approach to identifying, measuring, and eliminating hallucination.
Production-Ready implementation strategies: hands-on techniques for integrating Voyage AI embeddings, MongoDB Atlas retrieval mechanisms, and Galileo evaluation pipelines into your existing AI infrastructure.
Advanced hallucination reduction techniques: proven methods for improving context relevancy through optimized retrieval strategies, reranking algorithms, and evaluation workflows.
The enterprise AI landscape is undergoing a fundamental shift. While most companies spent 2023 and early 2024 building proofs-of-concept with RAG systems, 2025 is shaping up to be the year organizations begin to move AI agents into production at scale.
But there’s a problem: traditional observability tools weren't designed for autonomous systems that make decisions, call tools, and operate across multi-step workflows. When an agent hallucinates or makes a costly mistake, you need more than basic logging; you need real-time evaluation and adaptive guardrails that can intervene before damage occurs. This becomes even more crucial as you build multi-agent systems.
That's why I'm excited to announce that Galileo has joined MongoDB's AI Applications Program (MAAP) as the first and only Agentic Evaluation Platform in their ecosystem!
Why This Partnership Matters
MongoDB's MAAP combines the essential components needed for production AI: data management, LLM orchestration, model hosting, and now, with Galileo, comprehensive agent reliability. This isn't just another integration; it's recognition that agentic evaluation is now table stakes for enterprise AI deployment.
The timing couldn't be more critical. Capgemini's research shows that 10% of organizations already use AI agents, with over half planning adoption in 2025. However, without proper evaluation frameworks, these deployments often fail due to unpredictable behaviors, compliance violations, or security breaches. Thanks to these challenges, Gartner is already predicting that over 40% of agentic AI projects will be cancelled by the end of 2027.
Traditional observability solutions fall short because they can't keep pace with the non-determinism and dynamic failure modes inherent in agentic systems. A single bad tool call can expose sensitive data or incur significant costs, making real-time intervention essential, not optional.
What Galileo Brings to MAAP
Our platform addresses the unique challenges of agentic reliability through:
Real-time evaluations powered by our proprietary Luna-2 small language models that catch issues before they impact users
Adaptive guardrails that learn from your specific use cases and business context
Automated root cause analysis that helps teams understand not just what went wrong but why
Multi-agent observability designed for the complex interaction patterns that define modern AI systems
This isn't about retrofitting traditional monitoring tools for AI; it's purpose-built infrastructure for autonomous systems that need to operate reliably at enterprise scale.
For MongoDB's ecosystem partners and customers, this means being able to deploy agents confidently, knowing that potential issues will be identified and addressed proactively rather than reactively.
The future of enterprise AI isn't just about building smarter agents—it's about building agents you can trust. With MongoDB's comprehensive data platform and Galileo's agentic evaluation capabilities, that future is here.
Learn More
Want to see how MongoDB and Galileo work together to enable reliable agentic systems? Join our upcoming webinar on July 17th at 12 PM ET, where our Principal Developer Advocate Roie Schwaber-Cohen and MongoDB's Staff Developer Advocate Richmond Alake will demonstrate:
The complete AI reliability framework: a systematic approach to identifying, measuring, and eliminating hallucination.
Production-Ready implementation strategies: hands-on techniques for integrating Voyage AI embeddings, MongoDB Atlas retrieval mechanisms, and Galileo evaluation pipelines into your existing AI infrastructure.
Advanced hallucination reduction techniques: proven methods for improving context relevancy through optimized retrieval strategies, reranking algorithms, and evaluation workflows.
Conor Bronsdon
Conor Bronsdon
Conor Bronsdon
Conor Bronsdon