Agent Action Input Slot Extraction

Agent Action Input Slot Extraction

Activate when the agent invokes an Apex/Flow action correctly but with the wrong arguments — date misread, account id missing, picklist value paraphrased, phone number truncated. The skill produces tuned input-variable descriptions, utterance-level test cases, and a re-prompt policy for ambiguous or missing inputs.

Before Starting

Gather this context before working on anything in this domain:

• The action's invocable definition: each input variable's name, type, description, and required flag. The description is the primary signal the LLM uses to extract values; vague descriptions → wrong slots.
• Real user utterances. Synthetic test cases miss the verbal patterns users actually produce ("schedule it for next Tuesday afternoon, ish").
• Whether the action is a one-shot invocation or part of a multi-turn flow. Multi-turn allows re-prompts; one-shot must succeed-or-fail with the first utterance.

Core Concepts

How slot extraction works

When the agent recognizes that an action should be invoked, it constructs an LLM prompt containing:

• The action's invocable input variables and their descriptions
• The user's utterance(s)
• The conversation context

The LLM extracts values for each input variable and validates them against the input type. Missing required inputs trigger a re-prompt cycle.

The description field is the lever. A description like "Date" gets misread for any date in the utterance. "The customer's preferred appointment date in ISO 8601 (YYYY-MM-DD); reject relative phrases like 'next week'" constrains extraction.

Types and coercion

Re-prompt strategy

When a required slot can't be extracted, the agent re-prompts: "What date should I schedule the appointment for?". The re-prompt template should be configured per input. Without configuration, the agent generates a generic prompt that often confuses the user.

For ambiguous extraction (two plausible values), the better pattern is to confirm: "Did you mean Tuesday March 12 or March 19?".

Common Patterns

Pattern: explicit format constraint in description

When to use: Date, datetime, phone, id, anything with a canonical format.

How it works: Description includes the format and an explicit reject clause. "Account record ID; must be exactly 18 alphanumeric characters starting with '001'. Reject names or fragments."

Why not the alternative: A description of "Account ID" alone causes the LLM to hallucinate IDs from account names.

Pattern: enumerate picklist values inline

When to use: Apex enum or Flow choice as input.

How it works: Description lists every valid value with disambiguation. "Severity: one of LOW (cosmetic, no impact), MEDIUM (workaround exists), HIGH (production blocked). Synonyms: 'critical' = HIGH; 'minor' = LOW."

Why not the alternative: Without enumeration, the LLM matches user phrasing to the closest type member, often wrong ('urgent' → MEDIUM when policy says HIGH).

Pattern: name-to-Id resolution outside the LLM

When to use: Action takes a record Id but users speak in names.

How it works: Define the input as a String accountName. Inside the Apex/Flow action, resolve to Id via SOQL with proper escaping and ambiguity handling. Never let the LLM emit IDs.

Decision Guidance

Recommended Workflow

1. List every invocable input the action exposes. For each, ask: what's the canonical type? what synonyms or formats might appear? is it required?
2. Write a specific description per input. Include format, examples, reject clauses. Treat the description as the LLM's instruction manual for that slot.
3. Build a sample-utterance test set: 10–20 utterances per slot covering typical, edge-case, and adversarial phrasings.
4. For required slots, configure a re-prompt template that names the missing slot and gives an example.
5. For lookup-type slots, change the input from Id to String <name> and resolve inside the action; document the lookup ambiguity policy (first match? prompt for clarification? abort?).
6. Run the test utterances through the agent test harness (Agent Builder → Test in App). Record extraction accuracy per slot.
7. Iterate on the descriptions until extraction accuracy crosses your bar (typically ≥95% for high-stakes actions, lower for low-stakes).

Review Checklist

• Each input has a description that includes format + examples + reject clauses
• Picklist/enum inputs enumerate values with synonym disambiguation
• Lookup/Id inputs are taken as names, resolved inside the action
• Required-slot re-prompts configured with slot name and example
• Test-utterance suite covers ≥10 utterances per slot
• Accuracy measured and meets the action's stakes

Salesforce-Specific Gotchas

1. Description text is the primary lever; the variable name is secondary — String d with description "appointment date" extracts as well as String appointmentDate with the same description. Don't rely on naming.
2. Date inputs default to the running user's timezone — "next Tuesday" relative to which timezone? Specify in the description.
3. Required + no value extracted = agent emits a built-in re-prompt — Often phrased awkwardly. Always override.
4. Hallucinated IDs validate as the right shape but reference no record — Apex must check existence and surface a clear error to the agent loop, not silently fail.
5. Picklist values must match exactly — The LLM may emit "high" when the picklist value is "HIGH". Apex enum coercion fails. Normalize case in the action.

Output Artifacts

Related Skills

• agentforce/agent-actions — for the broader action design and invocation flow
• agentforce/agentforce-tool-use-patterns — for when an action call leads into another tool call
• agentforce/agentforce-eval-harness — for measuring extraction accuracy at scale
• agentforce/custom-agent-actions-apex — for Apex implementation patterns of name-to-Id resolution