The Execution Trace Fetch Tool
Fetch the step-by-step execution trace of an agent run — every tool call, input, and output — so you can debug, explain, and audit precisely how it got from prompt to answer.
Autonomy without guardrails is a liability
An agent that can act can also leak PII, expose a secret, exceed permissions, or run up cost — silently. Putting agents in production means baking in detection, redaction, permission checks, and traceability, or the first incident ends the program.
Data leakage
PII and secrets slip into prompts, logs, and outputs.
Over-permissioned
Agents act beyond what their role should allow.
No accountability
Without a trace, you can’t explain what an agent did or why.
Runaway cost
Unbounded tool use burns budget with no early warning.
Execution Trace Fetch, without the risk
Capability
What it does
Replay exactly how an agent reached its result.
it fetches the step-by-step execution trace of an agent run, including tool calls, inputs, and outputs.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
traces are recorded inside your perimeter and returned in order, so you can replay a run exactly — the observability that makes agent behavior debuggable and explainable.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
These controls run inside your perimeter and feed a single audit trail, so detection, redaction, permission checks, and cost limits are enforced locally — the governance layer that makes agent autonomy defensible to security and compliance.
Per-tenant, logged
Parameters
The execution_trace_fetch tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: true Optional Include tool inputs and outputs in the trace.
How the Execution Trace Fetch tool works in practice
Execution Trace Fetch is a security, governance & ops tool you assign to a VDF AI agent. It fetches the step-by-step execution trace of an agent run, including tool calls, inputs, and outputs. Its hallmarks — Trace, Replay, Observable — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, traces are recorded inside your perimeter and returned in order, so you can replay a run exactly — the observability that makes agent behavior debuggable and explainable. It expects run_id as required input, so calls are explicit and easy to audit. Every call is scoped to the requesting tenant and written to an audit log, so the capability is safe to run inside a regulated, on-premise environment — the same governance model behind every VDF AI tool.
Teams reach for Execution Trace Fetch when they need to handle debugging, explainability, and audits. It rarely works alone — pair it with Audit Trail Query, Tool Health Check, and Cost Estimator to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Execution Trace Fetch pays back
Debugging
See exactly where a run went wrong.
Explainability
Show how an agent reached an answer.
Audits
Provide a full record of a decision.
Improvement
Analyze traces to refine agents.
Assigned to agents, orchestrated as networks
On VDF AI, an industry’s use cases map to agents, and you assign tools like this one to those agents. Compose multiple agents into a governed, on-premise network.
What changes after you assign it
Questions about the Execution Trace Fetch tool
What is the Execution Trace Fetch tool?
It fetches the step-by-step execution trace of an agent run, including tool calls, inputs, and outputs. Assigned to a VDF AI agent, it runs under role-based policy with full audit logging so the capability is safe to use in production.
What’s in a trace?
The ordered steps of a run — each tool call with its inputs and outputs — so you can replay it exactly.
How is it different from the audit trail?
The audit trail records actions across the system; an execution trace is the detailed step log of one specific run.
What inputs does the Execution Trace Fetch tool need?
It requires run_id, and optionally accepts include_io. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Execution Trace Fetch?
Execution Trace Fetch is commonly assigned alongside Audit Trail Query, Tool Health Check, and Cost Estimator. On VDF AI you compose several tools and agents into a single governed, on-premise network.
Does it run on-premise?
Yes. Like every VDF AI tool, it can run on-premise or in your sovereign cloud, scoped per user and audit-logged, so your data never leaves your perimeter.
How do agents use it?
You assign the tool to an agent under a role-based policy; the agent calls it as one step in a task, and several agents and tools can be orchestrated together as a governed VDF AI Network.
Assign Execution Trace Fetch to these agents
These VDF AI agents can be assigned this tool. Open an agent to see the full toolkit it can run.
Tools that work well alongside this one
Where this tool delivers value
Put Execution Trace Fetch to work
See the Execution Trace Fetch tool assigned to an agent and orchestrated in a governed, on-premise network.