The Retry Job Tool
Retry a failed job or workflow — optionally from the failed step — so a transient error doesn’t throw away completed work, making automation resilient instead of brittle.
A one-shot agent isn’t automation
Real automation runs on a schedule, coordinates steps, retries on failure, and reports what happened. Without scheduling, triggering, retries, and notifications, an agent is a demo you have to babysit — not a system you can depend on.
Manual triggering
Someone has to kick off every run by hand.
Silent failures
A failed step stalls the whole job with no alert.
No retries
A transient error kills work that should have recovered.
No visibility
You can’t see what ran, when, or with what result.
Retry Job, without the risk
Capability
What it does
Re-run a failed job from where it broke.
it retries a failed job or workflow, optionally resuming from the failed step.
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
retries use the run’s durable state and checkpoints inside your perimeter, so recovery re-does only what failed rather than starting over — with backoff to avoid hammering a flaky dependency.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
Scheduling, triggering, and reporting run inside your perimeter with every job observable and logged, so recurring automation is reliable and auditable — the plumbing that lets a governed network run on its own.
Per-tenant, logged
Parameters
The retry_job tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: 3 Optional Maximum retry attempts.
How the Retry Job tool works in practice
Retry Job is a workflow & automation tool you assign to a VDF AI agent. It retries a failed job or workflow, optionally resuming from the failed step. Its hallmarks — Retry, Resume, Backoff — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, retries use the run’s durable state and checkpoints inside your perimeter, so recovery re-does only what failed rather than starting over — with backoff to avoid hammering a flaky dependency. 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 Retry Job when they need to handle transient errors, save work, and resilience. It rarely works alone — pair it with Workflow Status, Cancel Job, and Checkpoint Restore to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Retry Job pays back
Transient errors
Recover from a temporary failure automatically.
Save work
Resume rather than redo a whole job.
Resilience
Make long automation robust to blips.
Backoff
Retry a flaky dependency without hammering it.
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 Retry Job tool
What is the Retry Job tool?
It retries a failed job or workflow, optionally resuming from the failed step. 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.
Does it redo everything?
No. With checkpoints it resumes from the failed step, preserving completed work.
Can it back off?
Yes. Retries apply backoff and stop after max_attempts to avoid overwhelming a failing dependency.
What inputs does the Retry Job tool need?
It requires run_id, and optionally accepts from_step and max_attempts. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Retry Job?
Retry Job is commonly assigned alongside Workflow Status, Cancel Job, and Checkpoint Restore. 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.
Tools that work well alongside this one
Where this tool delivers value
Put Retry Job to work
See the Retry Job tool assigned to an agent and orchestrated in a governed, on-premise network.