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Datadog Introduces New AI Agents

Datadog introduced three new AI agents that perform interactive investigations and asynchronous code fixes for development, security and operations teams.

The launch of the Bits AI SRE, Bits AI Dev Agent and Bits AI Security Analyst agents, alongside the new Proactive App Recommendations and APM Investigator capabilities, marks the continued evolution of Bits AI, Datadog’s generative AI assistant that helps engineers resolve application issues in real time.

Datadog’s new domain-specific AI agents, unveiled at DASH, are each trained to be experts in incident response, product development and security. The agents are built on a flexible system of shared tasks—core capabilities such as querying data, analyzing anomalies or scaling infrastructure that can be reused across agents. The architecture allows Datadog to build and deploy new agents quickly while maintaining a consistent and powerful user experience. This is combined with expansive, high-quality observability data, enabling Datadog’s AI capabilities to operate with context and precision, and deliver insights and actions to eliminate risk.

“Datadog is uniquely positioned to deliver value with AI as a platform that has a wealth of clean, rich data—we process trillions of data points and are embedded in our customers’ critical engineering, developer and security workflows,” said Yanbing Li, Chief Product Officer at Datadog. “With these advancements in AI reasoning and multi-modality, we’ve gone beyond helping organizations understand their availability, security, performance and reliability. We now enable human-in-the-middle workflows by guiding customers on what to look for and where to start looking, and augment their ability to take action.”

The new AI agents announced at DASH are:

  • Bits AI SRE, a 24x7 on-call responder that is now in Limited Availability. For all alerts, Bits AI SRE performs early triage using telemetry and service context to surface initial investigation findings all before responders log in. It assigns appropriate owners, aligns all parties with real-time incident summaries and status updates, and proactively suggests next steps. It also generates a first draft of the incident post-mortem to save responders time.
  • Bits AI Dev Agent, now in Preview, which detects issues, generates code fixes, and opens pull requests tailored to organizations’ technology stack to allow users to quickly review and merge changes directly within their SCM. As a result, engineers don’t just have software that surfaces errors—they have an AI teammate that helps fix the issue and improve productivity for human resources.
  • Bits AI Security Analyst, which autonomously triages Cloud SIEM (security information and event management) signals, conducts in-depth investigations of potential threats and delivers reasoned resolution recommendations without human prompting. Bits AI Security Analyst, now in Preview, automates investigations and reduces response times, fundamentally transforming how organizations process security signals.

Additional Applied AI capabilities in preview include:

  • Proactive App Recommendations, which continuously analyzes telemetry Datadog already collects to suggest high-impact fixes and the next best action. Whether it’s optimizing a slow query, addressing inefficient code paths or catching recurring exceptions, APM Recommendations shows developers exactly where to improve performance, reduce errors and cut down resource usage—all before users are affected.
  • APM Investigator, to help engineers troubleshoot and resolve latency spikes faster. It automates a previously manual process by identifying bottlenecks, scoping impact, highlighting patterns across slow traces, suggesting likely causes and proposing a fix.

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Datadog Introduces New AI Agents

Datadog introduced three new AI agents that perform interactive investigations and asynchronous code fixes for development, security and operations teams.

The launch of the Bits AI SRE, Bits AI Dev Agent and Bits AI Security Analyst agents, alongside the new Proactive App Recommendations and APM Investigator capabilities, marks the continued evolution of Bits AI, Datadog’s generative AI assistant that helps engineers resolve application issues in real time.

Datadog’s new domain-specific AI agents, unveiled at DASH, are each trained to be experts in incident response, product development and security. The agents are built on a flexible system of shared tasks—core capabilities such as querying data, analyzing anomalies or scaling infrastructure that can be reused across agents. The architecture allows Datadog to build and deploy new agents quickly while maintaining a consistent and powerful user experience. This is combined with expansive, high-quality observability data, enabling Datadog’s AI capabilities to operate with context and precision, and deliver insights and actions to eliminate risk.

“Datadog is uniquely positioned to deliver value with AI as a platform that has a wealth of clean, rich data—we process trillions of data points and are embedded in our customers’ critical engineering, developer and security workflows,” said Yanbing Li, Chief Product Officer at Datadog. “With these advancements in AI reasoning and multi-modality, we’ve gone beyond helping organizations understand their availability, security, performance and reliability. We now enable human-in-the-middle workflows by guiding customers on what to look for and where to start looking, and augment their ability to take action.”

The new AI agents announced at DASH are:

  • Bits AI SRE, a 24x7 on-call responder that is now in Limited Availability. For all alerts, Bits AI SRE performs early triage using telemetry and service context to surface initial investigation findings all before responders log in. It assigns appropriate owners, aligns all parties with real-time incident summaries and status updates, and proactively suggests next steps. It also generates a first draft of the incident post-mortem to save responders time.
  • Bits AI Dev Agent, now in Preview, which detects issues, generates code fixes, and opens pull requests tailored to organizations’ technology stack to allow users to quickly review and merge changes directly within their SCM. As a result, engineers don’t just have software that surfaces errors—they have an AI teammate that helps fix the issue and improve productivity for human resources.
  • Bits AI Security Analyst, which autonomously triages Cloud SIEM (security information and event management) signals, conducts in-depth investigations of potential threats and delivers reasoned resolution recommendations without human prompting. Bits AI Security Analyst, now in Preview, automates investigations and reduces response times, fundamentally transforming how organizations process security signals.

Additional Applied AI capabilities in preview include:

  • Proactive App Recommendations, which continuously analyzes telemetry Datadog already collects to suggest high-impact fixes and the next best action. Whether it’s optimizing a slow query, addressing inefficient code paths or catching recurring exceptions, APM Recommendations shows developers exactly where to improve performance, reduce errors and cut down resource usage—all before users are affected.
  • APM Investigator, to help engineers troubleshoot and resolve latency spikes faster. It automates a previously manual process by identifying bottlenecks, scoping impact, highlighting patterns across slow traces, suggesting likely causes and proposing a fix.

The Latest

Enterprises today operate in a real-time environment where uninterrupted access to trusted data has become a baseline expectation for users, applications and automated systems. Traditional DataOps models, built on manual effort and human triage, cannot keep pace with this always active demand. AI agents are emerging as the operational backbone, ensuring consistent data availability, reinforcing trustworthiness and enabling a level of scale that manual processes cannot achieve ...

For decades, trust in the digital workplace rested on familiar signals. We trusted faces on video calls, voices on the phone, and emails that appeared to come from people we knew. These cues felt human and intuitive. They anchored how decisions were made, approvals were granted, and access was authorized. AI-powered deepfakes have quietly broken that model ...

Cloud migration was supposed to be a one-way door. For most enterprises, it turns out it isn't. Cloud data repatriation is a real and growing trend. A new survey ... finds that 89% of organizations plan to expand their on-premises infrastructure footprint over the next two years — and 75% have already moved at least some workloads back from public cloud in the past 24 months. The findings point to a broad rethinking of where data belongs ...

Over the past few years, large language models (LLMs) have revolutionized the software industry. Given their ability to excel at multi-step reasoning, LLMs have helped enterprises streamline workflows and adapt to the unknown. However, employing such models comes with sky-high costs, latency issues, and limited flexibility. In the realm of IT operations, it is generally wiser to employ smaller, domain-specific models instead ...

For years, DevOps teams operated under a simple assumption: collect enough telemetry, and you can find and fix any problem. That assumption is breaking down. Modern enterprises now operate across microservices, hybrid cloud environments, APIs, Kubernetes, and highly automated delivery pipelines. Releases happen continuously, dependencies shift constantly, and failures spread faster than teams can diagnose them ...

New Relic surveyed IT and engineering leaders from the media and entertainment (M&E) sector to understand what's working — and where challenges persist with their observability practices. The findings reveal how M&E organizations are navigating rising platform complexity, audience expectations, and AI-driven change. Below are five takeaways that stand out ...

Let me start with something I've seen play out more times than I can count. A team hits a wall with the cloud. Costs creep up, then spike. Performance starts to feel inconsistent. Someone in finance asks a simple question like "why did this double?" and nobody has a clean answer ... Maybe this isn't the right place for everything. That realization feels like a breakthrough, like you've identified the problem. In reality, you've just identified the starting line ...

In MEAN TIME TO INSIGHT Episode 24, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses network observability tool sprawl ... 

In cloud-native systems, scaling is often as simple as moving a slider. For on-premise databases, the stakes are different. Over-provisioning hardware is expensive. Under-provisioning leads to performance bottlenecks that are difficult to fix once the equipment is in the rack ...

When most people think about cybersecurity, they picture firewalls, encryption, and access controls — technical tools designed to protect systems and data. But beneath the technology lies a deeper set of principles about trust, decision-making, and resilience ... The best leaders don't eliminate risk. They manage it intelligently. And in many ways, cybersecurity offers a surprisingly useful playbook for doing exactly that ...