Agent Governance and Execution
hub Agent governance and execution

Govern and execute AI agents with control, resilience, and visibility

Coordinate agent actions through a governed execution layer that applies policies before action, supports durable workflows, and provides the lineage and observability required for enterprise operations.

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Key capabilities

Govern how agents plan, act, and complete work

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Agent Harness

Manage how agents plan, remember, use tools, and complete multistep workflows—with checkpoints, recovery, and approvals to keep work controlled and visible.

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Policy Compiler

Translate governance requirements into machine-enforceable rules that can be applied before and during agent execution.

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Policy Pushdown

Push identity, permission, and access policies into the execution path, enforcing controls close to the systems and data involved.

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Agent Runtime

Manage execution for agents and workflows with isolated environments, persistence, memory, integrations, and scalable resources for reliable production operations.

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Skills

Package instructions, tools, and behaviors as reusable skills with controlled discovery that exposes only capabilities relevant to the task and user's permissions.

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Orchestration

Define sequences, dependencies, parallel paths, and handoffs, routing context and outputs between agents and tools while maintaining control over progression.

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Lineage

Connect inputs, context, tool calls, and outputs across an execution, helping teams understand outcomes and support review, troubleshooting, and audit.

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Observability

Capture traces, logs, tool calls, errors, and performance signals across workflows to diagnose issues, improve reliability, and support optimization.

Use case

Automate complex work while maintaining governance and human oversight

Coordinate agent actions across data and tools while applying policies, approvals, and traceability throughout execution.

Agent workflow

Governed incident investigation and response

An operational issue triggers an agent workflow that gathers approved evidence, checks relevant system context, and coordinates analysis across specialized agents and tools. Policies restrict which data and actions are available, while sensitive steps pause for human approval. The execution remains recoverable if interrupted, and lineage and observability preserve a record of the context, tool calls, approvals, and outcomes used to complete the investigation.

Frequently asked questions

Teradata Agent Execution FAQ

Teradata Agent Execution is the execution layer that coordinates how AI agents use tools, follow policies, manage state, and complete work. It brings together the Agentic Harness, runtime, skills, orchestration, lineage, and observability needed for controlled enterprise execution. 

Agent frameworks help developers define agent logic and behavior. Agent Execution controls how that logic runs in production, including policies, permissions, isolation, approvals, workflow coordination, recovery, lineage, and operational visibility. 

The Agentic Harness coordinates how agents plan, remember, use tools, and complete multistep work. It also supports checkpoints, recovery, and human approvals for sensitive actions. 

Policies can be translated into enforceable controls and applied within the execution path. Identity, permissions, action limits, approvals, and other constraints help govern what an agent can access and do. 

Policy pushdown applies governance controls close to the systems, tools, and governed data involved in an action. This helps maintain existing access controls as agents execute work. 

Agent Runtime provides managed execution for agent workloads, including isolated environments, persistent state, memory, integrations, and scaling for supported single-agent and multi-agent workflows. 

Skills package reusable instructions and capabilities that agents can invoke for specific tasks. Controlled discovery helps ensure that agents see only the skills relevant to the task and permitted for the user. 

Orchestration coordinates sequences, dependencies, parallel activities, and handoffs across agents and tools. It also routes the context and outputs needed to move a workflow forward. 

Lineage and observability can capture inputs, context, policies, tool calls, approvals, execution status, errors, and outcomes. This gives teams the visibility needed to review, troubleshoot, audit, and improve agent workflows. 

Agent Execution is valuable when agents take actions, coordinate multi-step workflows, require human approvals, run for extended periods, or need reliable recovery and auditability. 

Put governed agent workflows into production with confidence

See how Teradata can help you coordinate agent actions, enforce enterprise controls, and maintain visibility from request to outcome. 

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