When outcomes are fuzzy, projects drift and sign‑off turns into a debate. Testable acceptance criteria solve this. They describe the exact state you expect to see when the outcome is achieved, where the evidence will live, and who confirms it. In this guide, you will learn how to write clear, observable criteria that speed decisions, reduce rework and make success unambiguous. We will cover a simple writing pattern, common errors, where your data should come from, reusable templates, and a practical sign‑off flow that balances agentic automation with human judgement. We will also show how criteria underpin Outcomes as a Service and Outcomes as Agentic Solutions (/kb/oaas-vs-oaas), so delivery is predictable and billing is fair.
What is a testable acceptance criterion in plain English?
A testable acceptance criterion is a single, unambiguous statement that names the system signal to check, the exact state to verify, the time window in which it must be true, the role that owns approval, and the evidence location where any reviewer can find proof. It turns a desired result into something a person or an agent can verify without interpretation. If you are still writing criteria as activity lists, revisit the distinction between outcomes and outputs (/learning-centre/outcomes-vs-outputs); outputs are activity counts, while outcomes are state changes with evidence.
How do you write criteria that both humans and agents can evaluate?
Write each criterion as a short sentence using this pattern: “Signal = State within Time, approved by Role, evidence at Location.” In practice that looks like: “Deal.invoice_status = ‘Posted’ and Deal.invoice_id is not null within 24 hours of quote_approved_at; approved_by = ‘Finance Ops’; evidence_link attached to the Deal timeline.” The structure helps a reviewer scan quickly and allows an agent to evaluate the condition reliably. If you use the phrase “definition of done”, make sure it resolves to testable, observable checks rather than generic phrasing (/learning-centre/outcome-acceptance-criteria-testable).
What common mistakes make criteria untestable, and how do you fix them?
The most common problems are vague verbs, hidden evidence, activity masquerading as outcomes, missing time windows and unclear ownership. Replace “configured” or “completed” with a named field and value in your system of record. Store artefacts on the record that represents the outcome rather than in private drives, so evidence is discoverable at sign‑off. Describe the desired state, not the steps taken to get there. Add a time window so urgency is visible and exceptions are easy to spot. Name the approver role, or define the auto‑approval rule, so acceptance is predictable. If criteria still need interpretation, they are not ready.
Where should your data and evidence come from so criteria are observable?
Your criteria should rely on signals that are stable and easy to inspect in your systems. In HubSpot, that means object properties such as Deal.stage, Deal.invoice_status, Company.domain, Ticket.priority, or a custom property you control. It also means logs that capture timestamp, actor, before and after values, and the outcome of each action. Artefacts, approved documents, invoices, meeting notes and training records, should be attached directly to the relevant record. External confirmations, such as finance responses, should appear as IDs or status codes on the record, not as screenshots in a chat thread. strutoIX (/kb/strutoix-role-in-outcomes) helps here by synchronising HubSpot with finance and other systems, exposing safe actions for agents, and writing timeline events and artefacts to the records where teams work; that makes criteria observable, approvals attributable, and verification repeatable.
What is the minimal data contract and log format for verifiable outcomes?
A minimal data contract for evaluating criteria includes the object_type (Deal, Company, Contact or Ticket), the record_id, the required_properties to check, the valid_values for those properties, the time_window as an ISO‑8601 duration, the approver_role, and the evidence_location (a URL or file ID). If a required property is missing or a value fails validation, raise a “Data readiness” task, assign an owner, set a retry schedule with backoff, and pause that branch until resolved. Standardise log entries to include timestamp, trigger_source (for example “quote_approved” or “score_threshold”), object_type, record_id, action, before_values, after_values, actor_type (agent or human), status and an evidence_link. A consistent log shape turns acceptance reviews into quick confirmations rather than investigations.
How do you adapt reusable templates for onboarding, quote‑to‑cash, support and routing?
You can lift these patterns into your own stack by substituting your objects, properties and time windows. For customer onboarding (/solutions/outcome-onboarding-automation), make the data state explicit: Company.domain is present, the onboarding Deal runs in the “Onboarding” pipeline from “In Progress” to “Complete” within five working days of kick_off_at, and approved_by and approved_at are set by the onboarding owner. Access and enablement should be verifiable as properties and artefacts: required users hold roles such as Admin or Standard, an access checklist is attached to the Company record, and an access_review_at timestamp is present. First value is specific and provable: workflow ‘X’ is Active, Deal.first_value_achieved equals Yes, a test_artifact_url is attached to the Deal, and the approver captures their decision.
For quote‑to‑cash (/solutions/outcome-quote-to-cash-handoffs), define the quote and invoice states precisely: the quote is generated from the “Standard SaaS” template, a quote_pdf_url is attached to the Deal, discount_rate is within policy or a finance approval is recorded with approved_by and approved_at; the invoice is created in finance, Deal.invoice_id is set, Deal.invoice_status is Raised within 24 hours of quote_approved_at, and totals match the quote within a tolerance you publish.
For support escalation (/solutions/outcome-support-escalation-closure), express routing, escalation and closure as states and timestamps: Ticket.priority equals P1 routes to the Tier 2 team within five minutes; if Ticket.status is not Resolved at four hours, an escalation note is posted and owner_team is set to Escalations; the Ticket closes with Ticket.status equals Closed, Ticket.root_cause populated, a closure note attached, and the support lead’s approval captured.
For lead‑to‑customer routing (/solutions/outcome-lead-to-customer-routing), specify qualification, assignment and conversion as verifiable steps: Contact.lead_score equals or exceeds your threshold, Contact.lifecycle_stage changes to MQL, Contact.owner_id is set by territory rule within 15 minutes, first_response_logged_at is captured within 30 minutes, and a meeting is created within two working days with the associated ID stored on the Contact. These examples work because they are observable, time‑bounded and owned, with evidence stored on the record.
How should you design criteria for agentic orchestration and human‑in‑the‑loop?
Agentic orchestration (/learning-centre/verify-agent-assisted-steps-safely) relies on criteria written as system states that an agent can evaluate. Prefer enumerations over free text for the states that drive decisions, status, stage, decision, so prompts and rules are robust. Add boundary rules such as retry counts and pause conditions when required fields are missing. Place human approvals where risk or customer experience changes, and capture the decision on the record itself using approved_by, approved_at and an approval_note. Keep the approver’s task short by presenting a concise summary of evidence and a clear choice, with a timer and a fallback owner to prevent stalls. If you need guidance on designing the approval touchpoints, see human‑in‑the‑loop checkpoints (/learning-centre/human-in-the-loop-checkpoints).
What does a practical sign‑off flow look like from prepare to publish?
A practical sign‑off flow has five predictable steps. First, the agent prepares by collecting logs and artefacts and presenting a short checklist aligned to your criteria. Second, the named approver reviews in one place, usually the record itself, rather than chasing files across systems. Third, the approver decides and records accept or reject; on rejection, the agent must create a corrective task that names the failed criteria, assigns an owner, and sets a retry_by timestamp, with the reason stored in approval_note. Fourth, the decision is recorded with timestamp and actor via approved_by and approved_at. Fifth, the outcome status is published and downstream actions unlock or pause automatically. Because the flow uses the same fields and artefacts every time, sign‑off becomes quick and consistent rather than a meeting.
What boundaries and exclusions prevent scope creep during acceptance?
A short, explicit set of boundaries prevents unplanned work entering acceptance by accident. State what is out of scope in simple terms and link it to your criteria. For example: “Data migration is limited to Company and Contact objects; Opportunity/Deal migration is excluded from the acceptance scope for this outcome.” Do the same for legal and accounting rules, regional differences and any dependencies that trigger separate approvals. When boundaries are clear, criteria stay tight and acceptance remains fast.
How do you keep governance simple without slowing delivery?
Good governance protects customers and data while preserving speed. Define least‑privilege roles and scopes up front, and promote configuration in reversible steps with a short change log. Keep acceptance tests short and observable; tie them directly to your success criteria (/learning-centre/metrics-that-prove-value-beyond-vanity) and store them with the record where evidence lives. Publish a brief pause and rollback runbook (/kb/pause-rollback-outcomes) that explains which triggers to suspend, how to drain pending queues, which fields are safe to reverse, for example reverting a stage if no invoice_id is present, and who approves re‑enablement. Make logs and artefacts searchable and retain them for an agreed period. strutoIX (/kb/strutoix-role-in-outcomes) supports this discipline by enforcing scopes, coordinating agent actions, and writing evidence to the records where teams already work.
How do acceptance criteria support OaaS and OaAS commercially and operationally?
In Outcomes as a Service you pay for verified successes rather than attempts, so criteria are the contractual definition of what counts as “done”. In Outcomes as Agentic Solutions, criteria are the rules agents use to decide, act and collect evidence safely. Together they align delivery with billing, make disputes rare and quick to resolve, and let you measure value with metrics that matter (/learning-centre/metrics-that-prove-value-beyond-vanity). If you need a wider context, see our outcomes guide (/learning-centre/outcomes-as-a-service-outcomes-as-agentic-solutions-guide).
Frequently asked questions
What is an acceptance criterion in simple terms?
An acceptance criterion is a short, unambiguous test that names the system signal to check, the exact state to verify, the time window, the approver role and where the evidence lives. If it requires interpretation, rewrite it until anyone can check it and reach the same conclusion.
How do I make criteria observable in HubSpot?
Use properties on the relevant object, such as Deal.stage or Deal.invoice_status, attach artefacts like PDFs and notes to the record, and log approvals via approved_by and approved_at. Keep all evidence on the record so sign‑off is fast and defensible.
Where should human approvals live?
Put approvals on the record that represents the outcome and capture the approver, timestamp and an approval_note. Do not rely on chat threads; they are hard to audit and easy to lose.
How do criteria support OaaS and OaAS?
OaaS uses criteria to define billable success, and OaAS relies on criteria so agents can evaluate states, act within rules, and collect evidence for quick sign‑off. That makes delivery predictable and pricing fair.
Where can I see criteria in action across common workflows?
For concrete examples, see our solution pages on Quote‑to‑cash (/solutions/outcome-quote-to-cash-handoffs), Support escalation and case closure (/solutions/outcome-support-escalation-closure), Lead‑to‑customer routing (/solutions/outcome-lead-to-customer-routing) and Automated customer onboarding (/solutions/outcome-onboarding-automation).
Conclusion
Testable acceptance criteria turn outcomes from aspirations into something you can prove. They keep teams aligned, make agentic orchestration safer, and turn sign‑off into a quick, repeatable step. Start with a simple pattern, anchor each criterion in a system signal, name the approver and the time window, and store evidence on the record that represents the outcome. The pay‑off is faster decisions, fewer disputes and a clean path to fair, success‑based billing, exactly what Outcomes as a Service and Outcomes as Agentic Solutions are designed to deliver.
