The Output Quality Evaluator Tool
Evaluate an agent’s output against explicit criteria — accuracy, completeness, tone, policy — and return a structured verdict, so quality is measured and enforced instead of assumed.
Autonomous agents fail quietly
An agent that can act is only useful if it remembers, plans, and checks its own work. Without a cognitive core, agents forget context, skip steps, and state wrong answers with full confidence — and you find out too late.
No memory across runs
Agents start from zero every session, re-asking what they were already told.
Acting before thinking
Without an explicit plan, agents take the first path, not the right one.
Confident wrong answers
Nothing checks the output, so mistakes ship as if they were facts.
No accountability
When it goes wrong, there is no trace of why the agent did what it did.
Output Quality Evaluator, without the risk
Capability
What it does
Grade agent output against your quality bar.
it grades an output against explicit criteria and returns a structured pass/fail verdict with reasons.
- Criterion-by-criterion scoring
- Structured verdict + reasons
- Enforces a quality bar
- Drives self-correction
Assignable to any agent
How it works
Predictable, inspectable behavior
Designed to be reliable.
it scores each criterion independently with justifications, so an agent can self-correct or a gate can block output that misses the bar — measurable, not vibes.
Every call logged
Governance
Private, governed, on-premise
Runs inside your perimeter.
This tool runs inside your perimeter, scoped per user with full audit logging, so the agent’s reasoning, memory, and decisions stay private and accountable — never sent to a third-party service.
Per-tenant, logged
Parameters
The output_quality_evaluate tool accepts these inputs when an agent calls it. Required inputs are flagged.
default: 0.7 Optional Minimum score (0–1) required to pass.
How the Output Quality Evaluator tool works in practice
Output Quality Evaluator is an agent core & quality tool you assign to a VDF AI agent. It grades an output against explicit criteria and returns a structured pass/fail verdict with reasons. Its hallmarks — Evaluate, Criteria, Verdict — let an agent rely on it as a dependable step in a larger task rather than a brittle one-off script.
Under the hood, it scores each criterion independently with justifications, so an agent can self-correct or a gate can block output that misses the bar — measurable, not vibes. It expects output and criteria as required inputs, 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 Output Quality Evaluator when they need to handle self-correction, release gates, and consistency. It rarely works alone — pair it with Answer Confidence Score, Fact Checker, and Citation Verifier to build a complete, governed workflow, then compose those steps into an on-premise VDF AI Network.
Where Output Quality Evaluator pays back
Self-correction
Let an agent grade and revise its own draft before returning it.
Release gates
Block output that fails accuracy or policy from shipping.
Consistency
Apply the same quality bar across every agent and run.
Eval loops
Score outputs at scale to improve prompts and 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 Output Quality Evaluator tool
What is the Output Quality Evaluator tool?
It grades an output against explicit criteria and returns a structured pass/fail verdict with reasons. 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.
Is this an LLM-as-judge tool?
Yes — it applies a governed, on-premise judge against your criteria, returning per-criterion scores and reasons you can audit.
Can it enforce a hard gate?
Yes. Set a threshold and route failing output to revision or human review instead of to the user.
What inputs does the Output Quality Evaluator tool need?
It requires output and criteria, and optionally accepts threshold. Each parameter is validated when an agent calls the tool, and the full call is logged for audit.
Which tools pair well with Output Quality Evaluator?
Output Quality Evaluator is commonly assigned alongside Answer Confidence Score, Fact Checker, and Citation Verifier. 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 Output Quality Evaluator to work
See the Output Quality Evaluator tool assigned to an agent and orchestrated in a governed, on-premise network.