AI build vs buy: How do you choose between custom tools vs vendors? | Diamond Bishop

What happens when you build AI agents trusted enough to handle production incidents while engineers sleep? At Datadog, it sparked a fundamental rethink of how enterprise AI systems earn developer trust in critical infrastructure environments.

Diamond Bishop, Director of Eng/AI, outlines for Ravin how their Bits AI initiative evolved from basic log analysis to sophisticated incident response agents. By focusing first on root cause identification rather than full automation, they're delivering immediate value while building the confidence needed for deeper integration.

But that's just one part of Datadog's systematic approach. From adopting Anthropic's MCP standard for tool interoperability to implementing multi-modal foundation model strategies, they're creating AI systems that can evolve with rapidly improving underlying technologies while maintaining enterprise reliability standards.

Topics discussed:

• Defining AI agents as systems with control flow autonomy rather than simple workflow automation or chatbot interfaces.
• Building enterprise trust in AI agents through precision-focused evaluation systems that measure performance across specific incident scenarios.
• Implementing root cause identification agents that diagnose production issues before engineers wake up during critical outages.
• Adopting Anthropic's MCP standard for tool interoperability to enable seamless integration across different agent platforms and environments.
• Using LLM-as-judge evaluation methods combined with human alignment scoring to continuously improve agent reliability and performance.
• Managing multi-modal foundation model strategies that allow switching between OpenAI, Anthropic, and open-source models based on tasks.
• Balancing organizational AI adoption through decentralized experimentation with centralized procurement standards and security compliance oversight.
• Developing LLM observability products that cluster errors and provide visibility into token usage and model performance.
• Navigating the bitter lesson principle by building evaluation frameworks that can quickly test new foundation models.
• Predicting timeline and bottlenecks for AGI development based on current reasoning limitations and architectural research needs.