AI & Agents
What a model or an agent is given and allowed to do: access to the code, the tools it may call, the retrieval behind its answers, and the guardrails around all of it.
Capabilities
Building an agent into your own application
Workspaces where people direct agents and work with their results
Checking model input and output against content rules
Getting structured data out of incoming documents
Calling a language model with tenant and credential handling solved
Training, deploying and calling predictive models on tabular data
Prompt templates stored and versioned apart from the code
Letting a coding agent read and act on your project or system
Giving an AI agent access to the systems you run over MCP
Checking an AI feature still behaves after a model or prompt change
Packaged knowledge that teaches a coding agent a framework
Grounding a model's answer in data retrieved at question time
Controlling what an agent may call, with an audit trail
Recent changes
All changes →Agent access to your code coverage reassessed
Agent evals coverage reassessed
Agent guidance coverage reassessed
Agent tooling coverage reassessed
Agent workspaces core coverage reassessed
Document grounding coverage reassessed