AI agent and LLM workflow validation

Does your agent work beyond the ideal user?

Construct tests AI agents, RAG, tools, memory and guardrails from the first runnable version to release. Try the method in your browser, then contact us to evaluate your real system.

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Task success · Agent trajectory · Repeatability · Context robustness · Uncertainty

See the full method — 37 secDefine behaviour → run controlled cases → compare contexts → inspect evidence.
Read the video transcript
  1. Define the behaviour and decision that the test must support.
  2. Use the public synthetic demo, or contact Construct to agree a real-system scope and controlled connection route.
  3. Run normal, boundary and repeated cases against the agent in its own environment.
  4. Capture observable results such as status, trajectory, latency and structured checks—not private chain of thought.
  5. Read technical and behavioural signals separately. Managed validation adds investigation and a human-written evidence report.

Start without buying anything

Try the method. Then decide if we should test your system.

There is no public price or checkout. The demonstration is immediate; work on your actual AI starts only after contact and written scope.

Public demonstrationYou test the method

Try Construct

Replay a small synthetic run or use the six cases with your own agent and record only the outcomes. Calculations stay in your browser.

  • AI-agent, research/persona and specialist-workflow lenses
  • Pass, latency, trajectory and critical-failure signals
  • Repeat agreement, context comparison, coverage and uncertainty
Open the 2-minute demo
Scoped serviceWe test with you

Validate your real AI

We define what the agent should do, run representative and boundary cases more than once, investigate failures and leave reusable evidence.

  • Private Metrics API when your team owns the protocol
  • Controlled API, local runner or guided session
  • Expert analysis and report when interpretation is required
Describe my systemRead the private API contract →

Commercial terms, timing, allowed data and any fee are agreed privately in a written proposal. The public demo is not a validation, audit, legal opinion or certification.

Real-system validation · connection routes

Three controlled ways to test your agent.

We choose the least-privilege route after the fit call. Your public-demo observations are never carried into the contact form.

A

API or test environment

You provide a temporary, limited token for staging. We execute agreed cases and record observable outputs, tool calls, errors, cost and latency; then you revoke access.

When an endpoint already exists.
B

Run in your environment

We prepare a small runner or container. Your team executes it locally or in your cloud and shares only the agreed result bundle.

When code or data cannot leave.
C

Guided test session

Without an API, we can use a temporary workspace, supervised session, batch export or anonymised traces. The reduced automation is documented.

When integration is unavailable.
Access stays limitedNo production password or raw personal data. Credentials are exchanged only after written scope, through the agreed secure method, and removed after testing.

The method

One claim. Controlled variation. Evidence you can rerun.

Psychometrics here means measurement discipline—not giving your AI a personality score.

01

Define

State the intended behaviour, users, environment and decision.

02

Design

Map normal, boundary, repeat and context cases to observable criteria.

03

Run

Test the final outcome and the agent trajectory more than once.

04

Interpret

Separate failure, variation and uncertainty; then decide what evidence is still missing.

Open the technical method →

Applications

One method, four different questions.

The measurement logic stays stable. The cases, references and human evidence change with the domain.

01

AI products and agents

RAG, copilots, tool-using agents and structured workflows.

02

Research and synthetic personas

Controlled scenario lenses across context, interaction and memory.

03

Specialist decision workflows

Rubrics, evidence use, uncertainty, handoffs and human decision boundaries.

04

Human–environment simulation

Populations, event phases, resources, feedback and adaptation.

Synthetic profiles expose possible failure modes; they do not represent or predict real people. Claims about users or populations require human evidence appropriate to the decision.

Test one agent. Learn which condition changes the result.

Use the public demonstration now. To connect your real system or receive private Metrics API access, contact Construct and we will define the smallest defensible scope.

Try ConstructTest my real agent