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Start with the work the bot must do
Write down the customer requests you want the bot to handle before comparing feature lists. Separate requests that need a straightforward answer from those requiring clarification, account-specific information, a change in another system, or a person’s judgment. This helps distinguish a bot that can answer common questions from one that can carry out a bounded support workflow.
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For each request, describe the expected outcome and what should happen if the bot cannot reach it. For example: a customer asks about a return, the bot uses the approved return policy, asks for an order detail if needed, and routes the conversation to an agent if the question depends on an exception. Treat that as a scenario to test, not as a capability to assume.
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Compare the features that determine whether a bot will work in practice
| Capability | What to establish | A useful acceptance test |
|---|---|---|
| Knowledge and answer controls | Which approved sources the bot can consult, how they are updated, and how answers are constrained to that information. | Ask a common question with a documented answer, then ask about an exception or a topic absent from the source. Check that the bot does not present an unsupported answer as certain. |
| Clarification and escalation | Whether the bot can ask follow-up questions, how a customer can request a person, and what conditions trigger transfer. | Try an ambiguous request, an explicit request for an agent, and a request outside the bot’s scope. |
| Handoff and continuity | Where a transfer goes, which conversation details the receiving agent sees, and what happens if the transfer cannot complete. | Transfer a conversation after several turns; confirm that the agent receives the transcript or other promised context and that the customer sees a clear next step. |
| Channels and conversation behavior | Which customer-facing channels are supported in the intended setup, and whether interactions are real-time or asynchronous and retain history as needed. | Start a conversation on each required channel, return to an existing conversation where supported, and check which history is available to the bot and agent. |
| Integrations and authorized actions | Which systems the bot can read or change, what authentication is required, and how access and actions are limited. | Test a permitted action, an action the bot should not be allowed to take, and an unauthenticated request for account-specific information. |
| Fallback and availability | What the customer sees when the answer is uncertain, an agent is unavailable, or routing fails. | Repeat a transfer when no agent is available and confirm the response gives a useful alternative rather than implying that a person has joined. |
| Analytics and review | Which conversation and operational signals the team can inspect to find failed answers, unresolved requests, or routing problems. | Check whether the team can locate a test conversation that failed and identify what should be corrected. |
| Plan, region, and configuration | Whether the necessary features are available for the specific plan, region, channels, and configuration being considered. | Get the vendor to confirm the intended setup and entitlements; documentation for one product or edition does not establish availability elsewhere. |
Ground answers in approved company knowledge
A support bot needs a defined basis for its answers. Ask whether it can use the sources your team actually maintains, such as a website, uploaded files, or knowledge-base content, and how updates to those sources reach the bot. Microsoft describes these kinds of sources for customer agents in Copilot Studio; Zendesk describes starting with trusted knowledge sources and extending bots with more advanced flows and integrations. Microsoft’s Copilot Studio overview and Zendesk’s chatbot options guide explain examples in those vendors’ ecosystems.
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Source access is not the same as answer correctness. Test questions with clear answers, vague wording, outdated material, and no supported answer. Notice whether the bot asks for clarification, indicates uncertainty, or routes the request instead of inventing a policy. Also consider who owns the source content and how changes to policies or procedures are reflected in it.
Make clarification and human handoff part of the design
Some questions are ambiguous; others need access, judgment, or authority the bot should not have. Decide what the bot should clarify, when it should offer a person, and which queue or team receives the conversation. A transfer should not make customers start over: check whether the receiving agent can see the conversation history and what details accompany it.
Handoff behavior can include more than the initial transfer. Zendesk documents how the first responder changes during a handoff and how handback depends on ticket status. Atlassian documents a chat setup in which an accepting agent can see the full transcript. These are implementation examples, not guarantees for every product or configuration. Test the transfer triggers, destination, retained context, and what happens if an agent is not available. See Zendesk’s handoff and handback guidance, Atlassian’s chat setup guidance, and Atlassian’s chat overview.
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Match channels to how customers actually seek help
Do not treat “supports chat” as proof that a bot fits every messaging workflow. Confirm the channels your customers use and whether conversations can continue across the interactions you need. Zendesk distinguishes real-time from asynchronous messaging and describes persistent conversations across a support site, help center, mobile apps, and third-party channels. Atlassian describes a support-site widget and embeddable widgets for other websites and apps. Those examples do not establish that every channel or cross-channel history is available in every plan. Zendesk’s messaging overview and Atlassian’s setup documentation describe their respective options.
For each required channel, verify how customers begin a conversation, whether they can return later, and what the agent can see when the bot escalates. A persistent asynchronous thread may suit a request customers resolve over time; a real-time interaction may be more appropriate when an immediate response is part of the service expectation. Choose based on the work and customer journey, not the channel label alone.
Set explicit boundaries for integrations and actions
A bot that can answer from a knowledge source is different from one that can retrieve account details or change a record. List every system the bot would need to read or update, then establish what access is permitted, how the customer or agent is authenticated, and how actions are constrained. Zendesk describes scripted dialogues, generative procedures, authorized actions, and API integrations; Amazon Connect describes agents that can answer questions, take actions, use knowledge bases, and escalate to people. See Zendesk’s chatbot options and Amazon Connect’s AI agent documentation.
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Use test cases that distinguish an allowed action from a prohibited one. Confirm that account-specific requests require the authentication your organization expects, that the bot cannot exceed its intended permissions, and that a failed or rejected action produces a clear customer-facing response. The appropriate controls depend on the systems and data involved; confirm requirements with the vendor and your security team rather than assuming a universal checklist.
Plan for failure, availability, and operational review
Every bot needs a useful path when it cannot answer or when a live agent cannot take over. Decide whether the customer should be asked for more detail, offered a different channel, given a status message, or told how to follow up. Test the wording as well as the routing: a message that implies a transfer succeeded when no agent received the conversation creates a misleading experience.
Analytics are useful when they help the team find what needs fixing. Ask what the team can review about unresolved questions, failed transfers, and conversations that did not reach an intended outcome. Zendesk identifies advanced analytics and user authentication among chatbot considerations. Atlassian documents a fallback message for cases when no agent is available and notes that live-chat availability depends on edition. These examples show why availability and operational features must be checked against the actual configuration; they do not establish a universal plan entitlement. Zendesk’s chatbot options guide and Atlassian’s setup guidance provide the vendor-specific details.
Use a short pilot to validate the workflow
- Choose representative requests. Include routine questions, ambiguous requests, exceptions, account-specific requests, and questions the bot should not answer.
- Prepare approved answers and boundaries. Identify the sources the bot may use, the actions it may take, and the cases that need a person.
- Run the same scenarios on required channels. Check whether the conversation style and history work for the actual customer journey.
- Test transfers and unavailable-agent cases. Inspect what the customer sees, where the request goes, and what context the receiving agent gets.
- Review the outcomes with support and security owners. Look for unsupported answers, broken routing, excessive access, and gaps in the signals the team can review.
- Confirm scope before rollout. Verify that the tested channels, integrations, controls, and operational features are available in the plan, region, and configuration you intend to use.
Questions to resolve before choosing a bot
- Which approved content sources can it consult, and how are those sources updated?
- Can it ask follow-up questions and recognize when a request should go to a person?
- Which events trigger escalation, where does the conversation go, and what history transfers?
- What does the customer see if an answer is uncertain, an agent is unavailable, or a transfer fails?
- Which channels, integrations, APIs, and authenticated actions are supported in the setup under consideration?
- What can the team review to identify unresolved questions and improve content or routing?
- What security, privacy, retention, and authentication requirements apply to the organization’s data and use case?
Frequently Asked Questions
Does connecting a knowledge base guarantee accurate answers?
No. A knowledge source gives the bot information to draw on, but it does not by itself establish that every response is correct. Test both supported answers and questions that the source does not answer, and decide how the bot should respond when evidence is missing.
Does every customer support bot need to take actions?
No. Some teams need a bot to answer questions and route harder cases; others may need it to interact with business systems. Action-taking adds access and authentication questions, so include it only where a specific workflow calls for it.
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No. Documentation describes capabilities in a vendor’s ecosystem, but it does not establish that a feature is included in every edition, region, or configuration. Confirm the scope for the setup you intend to use with the vendor.
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