AI agent quality assurance (QA) in Salesforce is the ongoing process of evaluating how Agentforce AI agents perform in real customer interactions. It helps organizations determine whether AI agents provide accurate responses, follow policies, complete the correct actions, handle escalations appropriately, and deliver consistent customer outcomes.

Testing an AI agent before deployment is essential, but it is only one part of AI quality management.

Once an AI agent begins interacting with real customers, organizations also need ongoing visibility into what that agent actually says, does, escalates, and resolves in production.

That is where AI agent QA becomes important.

Rather than asking only whether an AI agent performed correctly in a controlled test scenario, quality assurance helps organizations understand how the agent continues to perform across the wide range of interactions, exceptions, workflows, and customer situations it encounters after deployment.

Leaptree.AI develops Leaptree Optimize, a Salesforce-native contact center QA platform that helps organizations evaluate both human agents and Salesforce Agentforce AI agents within the same quality-management environment.

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What is an AI agent in Salesforce?

An AI agent in Salesforce is an autonomous or semi-autonomous AI system that can understand requests, reason about what to do, access relevant business data, and take actions using Salesforce workflows, applications, and APIs.

Salesforce Agentforce agents can use data, reasoning, and predefined actions to complete tasks. Depending on how they are configured, they may answer questions, perform business processes, interact directly with customers, update Salesforce information, and transfer work to human agents when necessary.

In a customer-service environment, an Agentforce AI agent might:

• Answer a customer's question
• Retrieve account or order information
• Update a Salesforce record
• Initiate a workflow
• Resolve a routine support request
• Escalate an issue
• Transfer a conversation to a human agent

This creates an important change for contact center quality assurance.

Traditionally, the “agent” being evaluated was a person. In an AI-enabled contact center, that is no longer always the case.

AI agents can participate directly in the customer journey and, in some cases, take operational actions that previously would have been completed by a human service agent.

That means they require quality oversight too.

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What is AI agent QA?

AI agent QA is the process of systematically evaluating AI-agent interactions and actions against defined standards for accuracy, compliance, process adherence, customer experience, escalation, and business outcomes.

In a Salesforce contact center, AI-agent QA may evaluate questions such as:

• Did the Agentforce agent provide the correct information?
• Did it use the correct process?
• Did it take the expected action in Salesforce?
• Did it stay within defined policies and guardrails?
• Did it escalate when it should have?
• Was the AI-to-human handoff successful?
• Did the customer ultimately receive the correct outcome?
• Is the same problem occurring repeatedly across many AI interactions?

The objective is not simply to determine whether an AI response sounds good.

An AI agent may produce a clear, fluent, and convincing response while still giving the wrong information, following the wrong process, taking an incorrect action, or failing to resolve the customer's underlying issue.

The more useful QA question is therefore:

Did the AI agent perform the job correctly?

That requires quality teams to evaluate both communication and execution.

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Why do Agentforce AI agents need QA?

Agentforce AI agents need QA because they can interact directly with customers and take business actions at scale. Errors can therefore affect far more than the wording of an individual response.

An AI agent may provide inaccurate information, but quality failures can also occur elsewhere in the service process.

For example, an AI agent might:

• Select the wrong process
• Perform the wrong action
• Miss a required step
• Use outdated information
• Fail to escalate an exception
• Transfer a customer without sufficient context
• Create an inconsistent customer experience

The more autonomy an AI agent has, the more important it becomes to evaluate both what it communicates and what it actually does.

This is particularly significant because AI agents can potentially handle large volumes of interactions continuously.

A recurring problem in an automated agent may therefore be repeated across many customer interactions before it is identified.

Quality assurance provides organizations with a way to monitor those interactions after deployment, identify recurring failures or risks, and feed what they learn back into the ongoing management and improvement of the AI agent.

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What is the difference between Agentforce testing and Agentforce QA?

Agentforce testing validates expected AI-agent behavior before or during deployment, while Agentforce QA continuously evaluates how AI agents actually perform across real customer interactions and operational outcomes.

Both practices are important, but they address different stages of the AI-agent lifecycle.

Agentforce testing

Testing asks:

“Will the agent behave correctly in this scenario?”

Agentforce testing can help organizations simulate interactions, evaluate agent behavior against expected outcomes, and identify problems before the AI agent is exposed to real customers.

Testing may be used to validate:

• Expected responses
• Topic selection
• Action selection
• Conversation behavior
• Response quality
• Predefined outcomes
• Different user scenarios

This is particularly useful during development, before deployment, and after important changes to an agent's configuration, instructions, knowledge, actions, or workflows.

Testing helps organizations establish whether the AI agent behaves as intended in the situations they anticipate.

Agentforce QA

QA asks:

“How is the agent actually performing across real customer interactions?”

Once an AI agent is live, organizations need visibility into what happens across production interactions that may not have been represented fully during testing.

Continuous QA can help identify:

• Recurring production failures
• Incorrect or inconsistent responses
• Policy and compliance issues
• Failed workflows
• Escalation problems
• Poor AI-to-human handoffs
• Customer-experience patterns
• Differences between expected and actual production behavior

Testing and QA therefore complement each other.

Testing helps organizations prevent problems before deployment. QA helps them identify, understand, and improve problems that emerge in production.

A mature AI-agent quality program needs both.

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Why isn't pre-deployment testing enough?

Pre-deployment testing is essential, but even extensive testing cannot reproduce every situation an AI agent will encounter once it begins interacting with real customers and live business systems.

Production interactions introduce complexity that can be difficult to model completely in advance.

Real customer conversations may involve:

• Unexpected wording
• Incomplete information
• Unusual requests
• Changing business data
• Combinations of scenarios developers did not anticipate
• Emotional or ambiguous conversations
• Evolving customer behavior
• Changes to connected systems and processes

AI systems may also produce variable behavior depending on the interaction, available context, data, instructions, or workflow involved.

An AI agent that performs correctly in a predefined test scenario may therefore encounter a different combination of circumstances once it is operating in production.

This makes continuous QA a natural complement to testing.

Organizations need to know not only:

“Did the agent pass our tests?”

but also:

“Is the agent continuing to perform correctly for actual customers?”

The second question can only be answered by observing and evaluating real-world performance over time.

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What should you QA in an Agentforce AI agent?

A strong AI-agent QA framework should evaluate more than response accuracy.

Because Agentforce AI agents can communicate with customers, access business information, initiate actions, complete processes, escalate interactions, and hand work to people, quality needs to be assessed across several dimensions.

1. Response accuracy

Did the AI agent provide correct information?

Accuracy is one of the most important dimensions of AI-agent quality.

QA may identify issues such as:

• Factually incorrect answers
• Unsupported responses
• Outdated information
• Contradictions
• Incomplete answers
• Misleading guidance

Generative AI can produce fluent responses that appear credible even when the underlying information is wrong.

That means quality teams cannot assess accuracy based solely on how natural or confident the response sounds.

The answer needs to be correct, appropriately supported, and relevant to what the customer actually asked.

2. Policy compliance

Did the AI agent follow the organization's rules?

Depending on the contact center and use case, this may involve requirements relating to:

• Mandatory disclosures
• Privacy requirements
• Authentication procedures
• Refund policies
• Regulatory processes
• Approval requirements
• Restrictions on what information can be provided

An AI response can sound helpful while still violating an important internal policy, compliance requirement, or business rule.

QA therefore needs to consider whether the agent operated within the boundaries established by the organization, not simply whether the customer received an answer.

3. Process adherence

Did the AI agent follow the correct process?

Many customer-service interactions depend on completing a sequence of steps rather than producing one correct response.

For example, an AI agent handling a refund request might need to:

1. Verify eligibility
2. Confirm customer information
3. Follow the appropriate refund policy
4. Perform the correct Salesforce action
5. Communicate the outcome accurately

An AI agent could communicate the final result clearly while still skipping or incorrectly completing an important step.

QA should therefore evaluate the process followed by the agent, not just the final sentence presented to the customer.

4. Action accuracy

Did the AI agent perform the correct action in Salesforce?

This becomes particularly important as customer-service AI moves beyond traditional chatbot functionality.

A chatbot may primarily provide information. An AI agent may also update records, retrieve business data, initiate workflows, create or modify cases, or complete other operational tasks.

As a result, AI-agent QA needs to examine not only what the agent said, but what the agent did.

For example, an Agentforce agent might tell the customer that an account has been updated.

The quality question is not complete until the organization can also determine whether the correct account update actually occurred.

A high-quality conversational response paired with the wrong operational action is still a quality failure.

5. Escalation behavior

Did the AI agent know when to stop acting autonomously and involve a human?

A mature AI agent needs clear boundaries around the situations it can handle independently.

QA should evaluate whether the agent escalates appropriately when it encounters situations such as:

• Requests outside its permitted scope
• High-risk customer issues
• Exceptions requiring human judgment
• Situations where required information is unavailable
• Repeated failures to resolve an issue

Escalation quality involves finding the appropriate balance.

An AI agent that escalates almost everything may create unnecessary work for human teams and reduce the value of automation.

An AI agent that continues autonomously when human intervention is required may create customer, operational, or compliance risk.

The QA framework should therefore assess whether escalation occurs when it should, and not simply whether escalation is technically possible.

6. AI-to-human handoffs

Did the customer move successfully from the AI agent to a human agent?

A technically successful transfer does not necessarily create a high-quality customer experience.

The handoff itself is part of the service journey and should be evaluated accordingly.

QA may need to ask:

• Was the handoff triggered at the right time?
• Was the customer routed to the right person or team?
• Did the human agent receive enough context?
• Did the customer have to repeat information?
• Did the handoff ultimately help resolve the issue?

An AI agent may correctly recognize that human intervention is needed but still create a poor experience if relevant context is lost during the transfer.

Quality therefore extends across the transition between AI and human agents.

7. Customer experience

Was the AI agent actually helpful to the customer?

Accuracy alone does not guarantee a good service experience.

An AI agent may provide technically correct information while still being:

• Confusing
• Repetitive
• Unnecessarily verbose
• Difficult to understand
• Poorly suited to the customer's situation

Customer-experience QA may therefore consider clarity, relevance, efficiency, conversational quality, and whether the agent appropriately adapted to the situation.

The goal is not to make an AI agent sound human for its own sake. It is to make sure the interaction helps the customer accomplish what they need with as little unnecessary friction as possible.

8. Outcome quality

Did the interaction achieve the intended customer or business outcome?

This may be one of the most important questions in AI-agent QA.

An interaction can appear successful at the conversational level while still failing to resolve the underlying customer issue.

For example, an AI agent may confidently tell a customer that a refund has been processed.

If the refund workflow did not actually complete, the interaction has not produced a successful outcome regardless of how good the conversation appeared.

Where relevant Salesforce operational data is available, QA can potentially connect the interaction with the resulting case, workflow, action, or outcome.

This allows quality teams to evaluate not only what happened in the conversation, but whether the service process actually worked.

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What are the biggest risks of AI agents in customer service?

The risks associated with AI agents depend on the use case, the level of autonomy given to the agent, and the business processes it can access.

However, several recurring areas are particularly relevant to QA.

Incorrect or unsupported responses

Generative AI may produce information that is inaccurate, incomplete, outdated, or unsupported by approved knowledge.

Because these responses can still sound convincing, organizations need quality controls capable of identifying incorrect answers rather than relying on conversational fluency as a proxy for accuracy.

Policy violations

An AI agent may provide an answer or perform an action that conflicts with an internal rule, compliance requirement, regulatory process, or defined guardrail.

The response may appear helpful to the customer while still creating organizational risk.

Failed actions

An AI agent may tell a customer that something has been completed even though the expected operational outcome did not occur.

This creates a particularly important distinction between conversation quality and execution quality.

QA needs to understand both.

Poor escalation

An AI agent may continue handling a situation that should have been transferred to a person, or it may escalate situations that it could have resolved successfully.

Both can create quality problems.

The goal is appropriate escalation based on the agent's scope, available information, risk, and the needs of the customer.

Failed handoffs

The AI agent may successfully transfer the interaction from a technical perspective while failing to preserve enough context for the human agent to continue effectively.

This can force the customer to repeat information or restart the support process, creating frustration even though the handoff technically “worked.”

Inconsistent experiences

Similar customer situations may produce different answers, actions, or outcomes.

QA can help organizations understand when these inconsistencies occur, whether they are acceptable, and whether they point to problems in instructions, knowledge, workflows, or agent behavior.

Errors at scale

One of the most significant differences between human-agent and AI-agent risk is scale.

A poorly performing human agent can affect the interactions they personally handle.

An automated AI agent may be able to repeat the same underlying problem across a far larger number of interactions.

This makes early detection and pattern identification particularly important for AI-agent QA.

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Should AI agents use the same QA scorecards as human agents?

Not necessarily. Human and AI agents contribute to the same customer experience, but they do not always need to be evaluated using identical criteria.

Some quality standards apply naturally to both human and AI agents.

These might include:

• Correct information
• Policy adherence
• Successful resolution
• Appropriate escalation
• Customer experience

However, the way those standards are evaluated may differ depending on the type of agent.

A human-agent scorecard might place additional emphasis on criteria such as:

• Listening skills
• Rapport
• Conversational empathy
• Coaching behaviors

An AI-agent scorecard may focus more heavily on areas such as:

• Grounded responses
• Approved knowledge usage
• Action accuracy
• Guardrail adherence
• Escalation rules
• Workflow completion

The strongest QA model therefore does not require identical scorecards for humans and AI.

Instead, it creates consistent quality standards across the contact center while allowing the evaluation criteria to reflect how each type of agent actually works.

Both agents may ultimately be responsible for helping the customer reach the right outcome, even if the behaviors that need to be evaluated along the way are different.

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How do you build an Agentforce QA scorecard?

A practical Agentforce QA scorecard should begin with the outcomes, responsibilities, and risks associated with the work the AI agent is expected to perform.

There is no universal Agentforce scorecard that will be appropriate for every organization or every AI-agent use case.

A customer-service agent that answers account questions may require different criteria from an agent that can issue refunds, modify records, complete approvals, or manage high-risk workflows.

Possible scorecard categories include:

Accuracy

Evaluate whether the agent provided information that was correct, relevant, and appropriately supported.

Questions may include:

• Was the response factually correct?
• Was the answer supported by approved information?
• Did the response address the customer's actual question?

Policy and compliance

Evaluate whether the AI agent stayed within the rules established by the organization.

Questions may include:

• Were mandatory policies followed?
• Were restricted actions avoided?
• Were required disclosures provided?

Process

Evaluate whether the agent followed the correct operational sequence.

Questions may include:

• Was the correct workflow followed?
• Were required steps completed?
• Was the correct action selected?

Escalation

Evaluate whether the AI agent recognized situations requiring human intervention.

Questions may include:

• Should the interaction have been escalated?
• Was escalation initiated at the correct point?
• Was the destination appropriate?

Handoff quality

Evaluate the quality of the transition from AI to human support.

Questions may include:

• Was relevant context preserved?
• Could the human agent continue without restarting the interaction?
• Was the customer's journey disrupted?

Outcome

Evaluate whether the service process ultimately achieved the intended result.

Questions may include:

• Was the customer's issue resolved?
• Was the promised action completed?
• Did the resulting Salesforce data reflect the correct outcome?

The exact scorecard should reflect the business process the AI agent is responsible for.

Rather than beginning with a generic list of AI metrics, organizations should begin by asking:

What does successful performance look like for this agent, and what failures would matter most?

The scorecard can then be built around those answers.

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Can you automatically QA Agentforce interactions?

Yes. Automated QA can evaluate large volumes of Agentforce interactions against predefined quality criteria and surface interactions that may require human attention.

This can be particularly useful because manually reviewing a small sample of AI-agent interactions creates many of the same visibility limitations as traditional human-agent QA.

A recurring problem may affect a large number of interactions while appearing in none of the conversations selected for manual review.

Automated Agentforce QA can expand coverage and help identify:

• High-risk conversations
• Failed evaluations
• Unusual behavior
• Policy violations
• Failed handoffs
• Poor customer outcomes

Human reviewers can then focus on the interactions that require deeper investigation, judgment, or intervention.

The objective should not be automation for its own sake.

It should be broader visibility combined with targeted human oversight.

This creates a model in which AI can help evaluate AI-agent performance at scale while quality teams retain responsibility for governance, investigation, calibration, and improvement.

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How does Leaptree Optimize QA Agentforce AI agents?

Leaptree Optimize is Leaptree.AI's Salesforce-native contact center QA platform for evaluating both Agentforce AI agents and human agents inside Salesforce.

Leaptree Optimize helps Salesforce contact centers apply quality criteria to AI-agent interactions across supported channels and bring those evaluations into the wider contact center QA process.

Organizations can use QA criteria designed specifically for AI-agent behavior and identify issues such as:

• Hallucinations or incorrect responses
• Failed handoffs
• Policy violations
• Inconsistent customer experiences
• Process failures

Because Leaptree Optimize also supports human-agent QA, quality teams can bring both parts of the service workforce into a shared quality-management environment rather than managing AI and human performance entirely separately.

This can provide a broader view of performance across the customer journey, particularly when customers move between AI agents, automated workflows, and human service teams.

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Why use the same QA environment for AI and human agents?

Customers do not experience an “AI operation” and a “human operation.” They experience one service journey.

A customer might begin with Agentforce, move through an automated workflow, transfer to a human agent, and later receive an automated follow-up.

From the customer's perspective, these are not separate systems. They are different parts of the same support experience.

A quality failure can occur anywhere along that path.

If AI-agent QA and human-agent QA are managed entirely separately, organizations may lose important context about what happens during the transitions between them.

A unified QA environment can make it easier to understand:

• How AI interactions perform
• How human interactions perform
• When AI hands work to humans
• Whether those handoffs succeed
• Where the overall customer journey breaks down

For example, an AI agent may perform correctly until the point of escalation, while the handoff itself causes the failure.

Alternatively, the AI interaction may appear unsuccessful in isolation even though the human-agent handoff ultimately leads to a strong customer outcome.

Understanding the complete journey therefore requires visibility across both.

Leaptree Optimize is designed to give Salesforce contact centers QA visibility across human and Agentforce AI-agent interactions within the same broader quality framework.

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Why does Salesforce-native AI-agent QA matter?

Agentforce does more than generate text.

It operates within Salesforce and can access business information, interact with workflows, and take operational actions.

That makes Salesforce context particularly valuable when evaluating AI-agent quality.

Consider an AI agent that tells a customer:

“Your refund has been processed.”

Conversation QA can evaluate whether that response was appropriate, clear, and consistent with policy.

But a more complete QA process should ideally be able to determine whether the refund process actually occurred.

When relevant Salesforce data is available, native QA creates an opportunity to connect:

What the AI agent said → what the AI agent did → what happened in Salesforce → what outcome the customer received

This creates a broader definition of quality.

Instead of treating the AI-generated response as the complete unit of evaluation, organizations can assess more of the service process surrounding that response.

That becomes especially important for agentic AI, where quality depends on both communication and execution.

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What is Leaptree.AI?

Leaptree.AI is the company behind Leaptree Optimize, a Salesforce-native, AI-powered contact center quality assurance platform.

Leaptree.AI helps Salesforce contact centers evaluate human and AI agents, automate appropriate QA processes, identify quality and performance issues, support more targeted coaching, and connect quality findings with operational improvement.

Its approach combines automated evaluation with human QA expertise, helping organizations increase quality visibility without removing the role of human judgment, governance, investigation, or coaching.

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What is Leaptree Optimize?

Leaptree Optimize is Leaptree.AI's Salesforce-native contact center QA platform. It combines AI-powered and human evaluation with customizable scorecards, automated flagging, calibration, coaching, analytics, data verification, and Agentforce QA inside Salesforce.

For organizations deploying Agentforce in the contact center, Optimize provides a way to bring AI agents into the wider quality-management framework rather than treating AI quality as a completely separate discipline.

Human and AI agents may require different evaluation criteria, but both ultimately contribute to the same customer journey.

A shared QA environment can provide quality teams with broader visibility across both.

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What metrics should you track for Agentforce QA?

The most useful Agentforce QA metrics depend on what the AI agent is responsible for and which risks or outcomes matter most to the organization.

Potential metrics include:

• QA pass rate
• Accuracy rate
• Policy adherence
• Escalation rate
• Correct escalation rate
• Handoff success
• Workflow completion
• Action accuracy
• Resolution rate
• Repeat-contact rate
• Recurring failure categories
• Customer-experience measures
• Volume of flagged interactions

Organizations should be cautious about reducing AI-agent quality to one overall score.

A single number may indicate whether performance has improved or declined without explaining what is actually changing.

For example, two AI agents could receive the same overall QA score while having very different underlying problems. One might struggle with response accuracy, while the other performs well conversationally but frequently fails to complete the correct Salesforce action.

A stronger QA program should therefore make it possible to understand not only whether quality is changing, but why.

Metrics should help teams identify the behaviors, processes, and outcomes driving overall AI-agent performance.

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How often should Agentforce AI agents be quality assured?

Agentforce QA should be continuous rather than treated as a one-time review.

An AI agent is not operating in a static environment.

Its performance can be affected by changes to:

• Prompts and instructions
• Knowledge sources
• Workflows
• Business policies
• Salesforce data
• Agent actions
• Integrations
• Escalation rules

Customer behavior can change as well.

New types of requests may emerge, language may change, processes may evolve, and the systems an AI agent depends on can be updated.

That means an agent that performed well during initial testing should not automatically be assumed to remain high quality indefinitely.

Testing, production QA, monitoring, calibration, and continuous improvement should therefore form an ongoing lifecycle.

Organizations need mechanisms for identifying when performance changes and determining whether those changes are caused by the agent itself, its data, its workflows, its instructions, or the wider service environment.

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What should happen when Agentforce QA finds a problem?

Identifying an AI-agent quality issue is only the beginning. A mature QA process should help organizations understand the scale, cause, and impact of the problem and determine whether corrective action actually resolves it.

When QA identifies an issue, teams should investigate questions such as:

1. What went wrong?
2. How often is it happening?
3. Which customers or processes are affected?
4. How serious is the issue?
5. What caused it?
6. What needs to change?
7. Did the change actually fix the problem?

The corrective action will depend on the underlying cause.

It may involve:

• Changing agent instructions
• Improving knowledge content
• Modifying a Salesforce workflow
• Adjusting an agent action
• Changing an escalation rule
• Updating the QA framework
• Adding additional tests
• Routing particular interactions for human review

This is why AI-agent QA should be viewed as part of a feedback loop rather than simply an evaluation process.

QA identifies what is happening in production. Investigation helps determine why it is happening. Corrective action addresses the underlying issue. Continued QA then provides evidence about whether the change worked.

Over time, this allows quality assurance to become part of the ongoing process used to govern and improve the AI agent.

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What is the future of Agentforce QA?

As AI agents take on a greater share of customer-service work, organizations will need to move beyond asking whether those systems are technically operational.

They will increasingly need to ask:

Are our AI agents performing well?

And, as human and AI agents become more interconnected:

Is the entire human-and-AI service operation producing the experience and outcomes we expect?

That changes the role of contact center QA.

Quality teams will increasingly need visibility across:

• Human-agent performance
• AI-agent performance
• AI-to-human handoffs
• Workflows
• Actions
• Customer outcomes
• Systemic failures across the complete journey

An individual interaction may move between an AI agent, an automated workflow, a human service agent, and additional operational processes before the customer's issue is resolved.

Quality therefore cannot always be understood by evaluating one participant in isolation.

The emerging model is broader: evaluate how the different parts of the service operation work together and determine where quality breaks down across the customer journey.

For Salesforce contact centers, this becomes particularly relevant as CRM data, AI agents, human agents, workflows, and digital service channels operate within an increasingly connected environment.

The future of contact center QA is not human QA or AI QA. It is continuous quality assurance across the entire human-and-AI customer journey.

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AI Agent QA in Salesforce FAQs

What is Agentforce QA?

Agentforce QA is the ongoing process of evaluating Salesforce Agentforce AI-agent interactions and actions against defined standards for accuracy, compliance, process adherence, escalation, customer experience, and business outcomes.

Unlike pre-deployment testing, production QA focuses on how the AI agent actually performs across real customer interactions after it has been deployed.

Does Salesforce have tools for testing Agentforce?

Yes. Salesforce provides testing capabilities for Agentforce that allow organizations to evaluate AI-agent behavior across defined scenarios before and during deployment.

Testing can help teams validate expected responses, action selection, workflows, and other agent behaviors before exposing changes to real customer interactions.

Is Agentforce testing the same as Agentforce QA?

No. Agentforce testing and Agentforce QA serve different but complementary purposes.

Testing primarily validates expected behavior in controlled scenarios, while continuous QA evaluates how the agent actually performs across real production interactions, workflows, handoffs, and outcomes.

Organizations can use both as part of an ongoing AI-agent quality lifecycle.

Can you automatically QA Agentforce interactions?

Yes. AI-powered QA can automatically evaluate eligible Agentforce interactions against defined criteria and surface conversations that may require further attention or human review.

Automated QA can provide broader visibility across AI-agent interactions while human reviewers retain responsibility for investigation, governance, calibration, and complex or high-risk decisions.

What should an Agentforce QA scorecard measure?

An Agentforce QA scorecard may evaluate response accuracy, policy compliance, process adherence, action accuracy, appropriate escalation, AI-to-human handoff quality, customer experience, and successful resolution.

The exact criteria should reflect the responsibilities, risks, and expected outcomes associated with the AI agent being evaluated.

Can you QA Agentforce voice agents?

Yes. AI-agent QA can evaluate Agentforce voice interactions where the QA platform has access to the relevant interaction and operational information.

The same broader QA principles apply: organizations may need to evaluate accuracy, policy adherence, process execution, escalation, handoffs, and customer outcomes rather than focusing solely on the words generated by the AI agent.

What is an AI-to-human handoff?

An AI-to-human handoff occurs when an AI agent transfers a customer interaction or task to a human agent.

QA can evaluate whether the transfer happened at the appropriate time, whether the customer was routed correctly, whether relevant context was preserved, and whether the human agent could continue the interaction without forcing the customer to restart the support process.

The quality of the handoff is part of the overall customer experience.

Why QA AI agents if they have already been tested?

Testing cannot anticipate every situation an AI agent will encounter in production.

Real customer interactions introduce unexpected language, unusual scenarios, changing data, edge cases, workflow variations, and other situations that may not have been represented during development.

Continuous QA helps organizations identify problems, recurring patterns, and production behaviors that emerge after the AI agent begins working with actual customers and business systems.

Can Leaptree Optimize QA Agentforce?

Yes. Leaptree Optimize supports QA for Salesforce Agentforce AI agents as well as human contact center agents.

It can help organizations evaluate AI-agent interactions and identify issues such as incorrect or unsupported responses, failed handoffs, policy violations, process failures, and inconsistent customer experiences within the wider Salesforce QA environment.

What is Leaptree.AI?

Leaptree.AI is the company behind Leaptree Optimize, a Salesforce-native, AI-powered contact center quality assurance platform for evaluating quality across human and AI agents.

What is Leaptree Optimize?

Leaptree Optimize is Leaptree.AI's Salesforce-native contact center QA platform. It combines automated and human QA with customizable scorecards, automated flagging, calibration, coaching, analytics, data verification, and Agentforce QA inside Salesforce.

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