Datadog has announced more than 100 new product capabilities across its observability, security and AI portfolio at DASH 2026, the company’s annual user conference, as organisations grapple with the operational challenges created by rapid AI adoption and increasingly complex technology environments.
The updates focus on automation, AI-driven operations, security monitoring and data management, reflecting growing enterprise demand for tools that can help manage expanding cloud, application and AI ecosystems.
According to Datadog, the new capabilities are designed to improve visibility across technology stacks while enabling greater operational autonomy through AI-powered workflows.
Expanded role for bits AI
A key focus of the announcement is the expansion of Bits AI, Datadog’s suite of AI-powered operational agents.
The company said the platform now extends beyond root-cause analysis to support autonomous detection, investigation and remediation of operational issues across infrastructure, applications and software development environments.
New capabilities include infrastructure monitoring, code analysis, release validation, testing, data analysis and AI agent evaluation. Datadog stated that the platform can continuously monitor environments, identify anomalies, recommend corrective actions and, where permitted, execute remediation workflows within predefined controls.
The company has also introduced support for evaluating and debugging AI agents, reflecting the growing adoption of agentic AI within enterprise environments.
Bits AI is integrated with collaboration platforms and developer tools, enabling teams to interact with operational workflows through existing work environments.
AI security moves into focus
Datadog also announced AI Guard, a new security capability designed to protect AI agents from emerging threats such as prompt injection, agent poisoning and behavioural manipulation.
As organisations deploy AI agents with access to business systems, data and external services, security vendors are increasingly focusing on protecting agentic workflows from attacks that may not be visible through traditional security controls.
According to Datadog, AI Guard combines behavioural analysis with telemetry monitoring to identify anomalous activity and detect threats that may emerge over multiple interactions rather than within a single prompt-response exchange.
The launch reflects broader industry concerns about securing autonomous AI systems as they become more deeply integrated into enterprise operations.
Addressing data growth challenges
To help organisations manage rising data volumes generated by AI applications and cloud-native environments, Datadog introduced Bring Your Own Cloud (BYOC).
The offering allows customers to deploy Datadog’s platform within their own cloud environments while storing and processing telemetry data using their own cloud object storage infrastructure.
The company said the approach is intended to provide greater flexibility around data management, retention and cost control as log volumes continue to grow.
Data storage costs have become an increasing concern for organisations deploying AI-driven applications, which often generate significantly larger volumes of operational and observability data.
Monitoring AI agents and development tools
Datadog also introduced Bits Agent Builder, enabling organisations to create custom AI agents for operational workflows such as incident response, remediation and reporting.
Complementing this capability is Agent Console, a new monitoring platform designed to provide visibility into enterprise AI agents and AI-assisted development tools.
The platform supports monitoring of coding and productivity assistants, including tools such as Claude Code, Cursor and GitHub Copilot, helping organisations understand adoption patterns, usage trends and operational outcomes.
According to Datadog, the objective is to provide greater visibility into how AI agents contribute to software development processes and operational workflows while enabling organisations to measure business value and resource utilisation.
Growing demand for autonomous operations
The announcements reflect a broader shift across enterprise IT towards autonomous operations, where AI systems increasingly assist with monitoring, troubleshooting and remediation tasks.
As software development accelerates through AI-assisted coding and enterprises deploy larger numbers of AI agents, operational complexity is becoming a key challenge for technology teams.
Industry analysts note that organisations are increasingly seeking platforms capable of providing unified visibility across applications, infrastructure, security systems, data environments and AI workloads while automating routine operational tasks.
Datadog’s latest updates position the company within this emerging category of AI-driven operational platforms, where observability, security and automation are converging to support increasingly distributed and AI-enabled technology environments.