Top 10 Agentforce Interview Questions That Are Filtering Candidates FAST

Agentforce has quickly become one of the most in-demand Salesforce skills — and interviewers are raising the bar. Whether you’re building intelligent automations, integrating Apex action handlers, or managing grounding to reduce hallucinations, Agentforce now requires real expertise, not just buzzwords.

If you’re preparing for interviews or evaluating candidates, these are the 10 most important Agentforce questions that are helping recruiters instantly identify strong vs. weak talent.

1️⃣ What is the full lifecycle of an Agentforce Action?

Every Agentforce Action follows a predictable lifecycle:

Intent Detection – The agent determines the user’s goal.
Planning – Agent selects the correct action type and parameters.
Execution – The action handler executes the required Apex/Flow/Integration.
Observation – Agentforce receives the output, success messages, or errors.
Reflection – The agent decides whether another action is needed.
Completion – The interaction ends with a safe, validated response.

A candidate who can explain this sequence clearly understands how Agentforce really works under the hood.

2️⃣ How do Agent Instructions override system behavior?

Agent Instructions act as policy-level constraints that can override default agent behavior. These can be:

System instructions
Developer-provided instructions
User-provided instructions

They control tone, guardrails, allowed actions, restricted data, and response quality. A strong candidate can explain how instructions stack and how they influence LLM outputs.

3️⃣ What is the difference between Tasks, Messages & Turns?

Understanding these is essential for debugging:

Messages → Individual communications between user, agent, or system.
Tasks → Larger units of work triggered by user intent.
Turns → One cycle of user input + agent response.

Top candidates explain how Agentforce bundles multiple messages into a turn, and multiple turns into a task.

4️⃣ Explain the Instruction Hierarchy with an example

Agentforce instruction hierarchy controls how rules are applied:
System > Developer > User
Example:

System: “Never reveal internal IDs.”
Developer: “Respond concisely in bullet points.”
User: “Explain in long form.”

The agent will follow:

The System instruction first
The Developer rule second
The User preference last

This tests a candidate’s practical understanding of LLM governance.

5️⃣ How does Agentforce decide which prompt template to use?

Agentforce evaluates:
✔ Current intent
✔ Task type
✔ Past conversation context
✔ Grounding data
✔ Active instructions

Then selects the best-fitting prompt blueprint. Candidates should mention template sets like:

Task execution templates
Search/retrieval templates
Conversational templates
Safe completion templates

6️⃣ What is Grounding and how does it prevent hallucinations?

Grounding is the process of validating LLM responses using real Salesforce data.
Agentforce does this using:

Dynamic queries
Context windows
Recent conversation memory
Object records
Metadata

Grounding ensures the response is tied to factual data — drastically reducing hallucinations.

7️⃣ How do Action Handlers work in Apex?

Action Handlers are Apex classes implementing:
implements AgentActionHandler

They include:

An invoke() method
Parameter parsing
Business logic execution
A return payload

Candidates should demonstrate knowledge of:
✔ Error management
✔ Limits
✔ Returning structured results
✔ Handling long-running tasks

8️⃣ How do you debug Agentforce when an action fails?

Top developers know to check:

Action Logs
Execution Trace (Tasks → Turns → Messages)
Payload Input/Output
Grounding Data
Apex Exception Logs
Prompt Template Selection
Instruction Conflicts

Debugging requires analyzing both LLM behavior and Apex handler logic.

9️⃣ What is Safe Completion and why is it important?

Safe Completion is Agentforce’s final guardrail. It ensures that before a response is sent back to the user:

Sensitive data is redacted
Policies are followed
Any hallucinations are corrected
No internal errors leak
Output is stable and compliant

This is critical for enterprises using AI in regulated environments.

🔟 How does Agentforce interact with the Einstein 1 Context Window?

Agentforce uses the Einstein 1 Context Window to retrieve:

Relevant records
User context
Metadata
Previous conversation data
Grounding snippets

This gives the agent structured, real-time CRM awareness, which boosts accuracy and enables deeper automation.

🎯 Final Thoughts

Agentforce is evolving fast — and these questions separate candidates who only “used” Agentforce from those who truly understand how to build, optimize, and govern intelligent agents inside Salesforce.