ABOUT THIS FEED
The DataRobot Blog is run by DataRobot, a leading enterprise AI company specializing in machine learning automation and AI-driven business solutions. Its RSS feed publishes case studies, product updates, and thought leadership pieces that explain how organizations use AI to solve real-world problems. Topics frequently include predictive modeling, MLOps, responsible AI, and best practices for scaling machine learning in enterprises. While it serves as a corporate platform, the blog also provides genuinely useful educational material, often written by DataRobot’s data scientists and engineers. Readers can expect insights into practical implementation strategies across industries such as finance, healthcare, and retail. With a weekly posting frequency, this feed is well-suited for business leaders, data scientists, and IT professionals seeking applied knowledge on AI deployment within large-scale organizations.
- The first 30 days of agentic AI governance: A practical checklist
Every agent you deploy expands your blast radius. A predictive model can produce a bad response, but an agent can act on it. Agents can retrieve sensitive data, change systems of record, trigger workflows, or pass errors to other agents. The risk is no longer just model quality. It is the authority an agent holds,... The post The first 30 days of agentic AI governance: A practical checklist appeared first on DataRobot.
- Identity as a lifecycle, not a setting
Agents are not configured once and left alone. They get built, published, deployed, and retired. An identity that does not track that whole arc is a record you wrote and forgot, which is how you end up with credentials outliving the agents they belonged to. So treat identity as a lifecycle. Credentials provision at a... The post Identity as a lifecycle, not a setting appeared first on DataRobot.
- Govern natively, federate outward, and what breaks across trust domains
By now the agent has its own identity and you can carry that identity through a chain of calls. The next question is where the rules live. Who decides what an agent is allowed to do, and where does that decision get made? Two answers, and they are load-bearing for everything above them. The authorization... The post Govern natively, federate outward, and what breaks across trust domains appeared first on DataRobot.
- Credentials should never reach the model
An engineer wires an agent to a payments API. The agent needs the API token, so the token goes where tokens usually go: an environment variable, a config file, or straight into the prompt. The agent reads it and makes the call. It works. It also just placed a live credential inside the one component... The post Credentials should never reach the model appeared first on DataRobot.
- DataRobot OpenCode: your coding agent, your model choice
There are over 70 coding agents on the market: Claude Code, Codex, Cursor, Copilot, Devin, and a long tail of smaller entrants showing up in engineers’ newsfeeds every week. Someone runs a benchmark, posts the leaderboard, and by the end of the week, half the org wants to try whatever is in first place. The... The post DataRobot OpenCode: your coding agent, your model choice appeared first on DataRobot.
- Delegation chains, the confused deputy, and the protocols you actually deploy
Agents rarely act alone. A user asks an agent to do something. The agent calls a tool. The tool calls another agent. By the time work gets done, three or four actors have touched the request, each acting on behalf of the one before it. Identity has to survive that chain. At every hop, you... The post Delegation chains, the confused deputy, and the protocols you actually deploy appeared first on DataRobot.
- AI agent governance at scale: from 5 agents to a 500-agent workforce
Governing 5 agents is a review process. Governing 500 agents is an infrastructure problem. Manual reviews and team-level approvals work when a handful of agents are visible and closely watched. Once agents spread across business units, tools, and environments, that oversight breaks down. Enterprises need an AI agent governance model that includes centralized identity, reusable... The post AI agent governance at scale: from 5 agents to a 500-agent workforce appeared first on DataRobot.
- What a first-class agent identity actually is, and whether it is just workload identity
The previous post left you with a borrowed credential and a non-deterministic actor that a static grant cannot govern. The fix is to stop borrowing. Give the agent a stable, verifiable runtime principal you can authorize against, attribute actions to, and revoke on its own. That sentence hides four requirements. Pull them apart. The four... The post What a first-class agent identity actually is, and whether it is just workload identity appeared first on DataRobot.
- Your agents are using your credentials, and that is the problem
An engineer ships an agent to production. It needs to call an internal API, so it uses the key already sitting in the engineer’s environment. The agent runs. It also now holds every permission that engineer holds. That is the default state of most agent deployments today. The agent has no identity of its own,... The post Your agents are using your credentials, and that is the problem appeared first on DataRobot.
- Your identity stack was built for two kinds of actor. Agents are a third.
An engineer ships an agent to production this week. It needs to call an internal API, so it uses the key already sitting in the engineer’s environment. The agent runs. It also now holds every permission that engineer holds. That is the default state of most agent deployments today. The agent has no identity of... The post Your identity stack was built for two kinds of actor. Agents are a third. appeared first on DataRobot.