What are common failure modes when building Agentforce agents and how do you diagnose them?

Common failure modes and diagnostic approaches:

1. Wrong topic classification: Symptom — agent invokes the wrong set of actions or gives irrelevant responses. Diagnosis — review the conversation log's topic classification trace. Fix — rewrite topic descriptions to be more specific and add explicit exclusions for commonly confused intents.
2. Action invoked with wrong parameters: Symptom — Flow or Apex receives null or incorrect input values. Diagnosis — examine action input values in the conversation trace. Fix — improve @InvocableVariable or Flow variable descriptions so the LLM correctly maps conversation context to parameters.
3. Action failure / Fault path triggered: Symptom — agent reports it cannot complete the request. Diagnosis — check Flow debug logs or Apex debug logs for the error. Fix — add better error handling and return meaningful error messages via output variables.
4. Hallucinated responses: Symptom — agent provides plausible but incorrect information. Diagnosis — check if the relevant Action was actually invoked; if not, the agent generated the response from training data. Fix — ensure a Knowledge or data retrieval action exists for the relevant topic and that grounding is correctly configured.
5. Scope creep / out-of-topic responses: Symptom — agent responds to requests it should escalate. Fix — tighten topic scope descriptions and ensure a fallback escalation topic is configured.