Agent lifecycle
Agentforce testing validates behaviour, not just configuration.
AI agents are non-deterministic. The same goal can produce different reasoning paths, actions and answers. Salesforce testing therefore needs conversation, classification, knowledge, security and business-outcome evaluation.
Quick answer
How should Agentforce be tested before launch?
Combine expected cases, exceptions, misuse attempts, user roles and production-like data. Agentforce Testing Center supports evaluation at scale, but useful scenarios and measurable acceptance criteria remain essential. After launch, session tracing, analytics and recurring regression are part of the product lifecycle.
Build a library of behaviours the agent must pass.
Start with process outcomes and risk. A service agent needs tests for grounded answers, citations, intent recognition, escalation and case updates. Sales scenarios add qualification, duplicates, consent and accurate CRM updates.
Each case specifies the expected topic, allowed actions, answer criteria and stop condition. Separate suites cover ambiguity, missing data, prompt injection, topic switching, duplicate execution and integration failure.
- Happy paths and real conversations
- Edge cases and negative tests
- Roles, languages and permissions
- Actions, integrations and side effects
Scale evaluation beyond manual builder conversations.
Agentforce Testing Center can generate or import cases and evaluate classification, action sequences and response quality. Results expose patterns that a single manual conversation will miss.
Automated scoring is calibrated against examples reviewed by process owners. For critical actions, a passing conversation test never replaces data controls and validation in Flow, Apex or an API.
Measure agent and process effectiveness after go-live.
Agentforce Observability supports analysis of sessions, traces, unresolved interactions, knowledge gaps and effectiveness metrics. We connect those signals to process KPIs such as deflection, handle time, update accuracy, conversion, escalation and cost per outcome.
Monitoring drives a controlled improvement loop: prioritise issues, adjust instructions, knowledge or actions, run regression and release a new version only when it passes.
Architecture scope
What an Agentforce quality plan covers
- 01Test strategy and acceptance criteria
- 02Scenario and test-data library
- 03Scaled test configuration
- 04Security and permission tests
- 05KPI and consumption dashboard
- 06Regression and optimisation process
FAQ
Testing Center and observability questions
01Are Agent Builder tests enough?
No. They support iteration, but production also needs repeatable suites, scaled tests, UAT, integration validation and permission checks.
02How many test cases are required?
There is no universal count. Cover priority intents, actions and risks, then expand until additional tests stop revealing new classes of failure.
03How is answer quality measured?
Combine factual accuracy, grounding, completeness, instruction adherence, tone, escalation and the resulting process outcome.
04Should we retest after launch?
Yes. Data, knowledge, instructions, models and integrations change, so regression belongs in the ongoing lifecycle.
Free pre-consultation
Find your first agentic use case.
30 minutes with a Salesforce architect. We will look at the process, data and risk. You leave with a concrete recommendation for the next step.
- No sales deck
- Initial readiness view
- A recommendation: pilot, discovery or not yet
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