Yes. Replay anonymised support conversations against both tool sets before switching, using isolated test records and agreed outcomes. Compare whether each version resolves the request correctly, avoids incorrect actions and hands unresolved cases to a person with enough context. A convincing reply alone is insufficient evidence.
For businesses reviewing AI chatbots, this creates a practical change decision: switch, repair and retest, or retain the existing setup. The workflow below is a proposed testing method, not a claim that every chatbot product includes conversation replay.
Define exactly what you are changing
Write down what “new tools” means: a replacement helpdesk, different order lookup functions, a new chatbot platform, or changed tool descriptions. Record the existing and proposed versions. If you also change the model, instructions and knowledge content, the comparison measures that whole package; it cannot isolate which change caused an improvement or failure.
Choose a narrow support job, such as checking delivery status and raising a delivery query. Define success before testing: the customer receives the recorded status, no unsupported delivery promise is made, and unresolved queries reach the correct queue.
Map equivalent business operations across both setups. “Find order” may have different names or inputs, but it should consult equivalent test records. Mark unsupported operations explicitly rather than quietly removing difficult cases. Our AI agents versus automation comparison helps distinguish decisions made by the assistant from steps fixed by the workflow.
Anonymise without removing the difficulty
Replace names, contact details, addresses, account references and identifying free text. Check attachments, quoted email chains and tool results too. Use consistent fictional replacements throughout each case so an order reference still matches its test record.
Removing names alone is not enough for this test preparation. A distinctive complaint, workplace or delivery location may still identify someone. Ask the person responsible for customer data to approve the prepared material and its intended use; do not assume a renamed transcript is safe to share.
Preserve details that affect behaviour: message order, corrections, missing fields, conflicting references and whether the customer requested a person. Replace exact dates where needed while retaining relevant elapsed intervals. For example, an old unanswered message should not become a fresh enquiry merely because the replay runs today.
Keep the original transcript outside the replay pack. Where realistic context cannot be retained safely, write a synthetic case and label it accordingly.
Replay turns against controlled records
Start both versions with equivalent test data and a fresh conversation. Feed customer messages in sequence, allowing each version to generate its own replies and tool requests. Do not insert the old assistant’s answers as if the new version produced them.
Historical conversations can branch awkwardly: a customer’s “yes” may answer a question only the old assistant asked. Define permitted customer responses in advance, or have an operator follow a fixed role script. Mark unmatched branches for review rather than forcing an incoherent transcript through the system.
OpenAI documents tool calling as a sequence in which the model requests a tool, the application executes it, and the result returns to the model. Consequently, retain evidence from each stage rather than treating a requested action as completed. Source: OpenAI function calling
First use controlled tool responses to compare conversation decisions. Then test the actual connections against isolated records to check retrieval, updates and handover. Label simulated results clearly. Block live messages and record changes, reset test state between runs, and record any unavailable integration as untested.
Use this support-tool change test plan
Copy this plan into your test document. Its release rules are proposals for the support owner to accept or adjust before running the comparison.
Decision: Should we replace the existing chatbot tool set for delivery-status enquiries and delivery-query handover?
Owners: Support owner: ___ | Test operator: ___ | Integration owner: ___ | Decision date: ___
Comparison: Existing version: ___ | Proposed version: ___ | Model and instructions: ___ | Test-record snapshot: ___
Isolation: Use fictional records, internal recipients and a test queue. Disable production writes and customer sends. Reset records and conversation state before each run.
| Case | Fixed starting condition | Expected behaviour | Evidence to retain |
|---|---|---|---|
| Ordinary lookup | Verified test customer; matching order; recorded status | Return that status without inventing a delivery date | Messages, lookup input and returned record |
| Missing reference | Customer asks about an order without identifying it | Ask for the approved identifying information; leave records unchanged | Clarification and action log |
| Ambiguous match | Lookup returns multiple eligible orders | Ask the customer to distinguish them; do not choose silently | Returned matches and follow-up |
| Duplicate message | Same delivery-query request arrives again | Check for an existing query before creating another | Message references and query records |
| Tool unavailable | Order lookup returns an error | Explain the limitation and offer the agreed human route | Error, customer reply and handover record |
| Human requested | Customer explicitly requests an operator | Route to the test queue with relevant context | Queue entry, summary and operator receipt |
Run procedure: Feed the same customer turns to each version. Use predefined responses for clarification branches. Record deviations. Repeat cases using a repetition count agreed in advance: ___.
Result fields: Case reference; version; run reference; expected outcome; actual reply; requested action; recorded action; resolution result; incorrect-action result; escalation result; evidence link; reviewer notes.
Proposed decision rule: Block switching for wrong-record actions, unsupported completion claims, duplicate queries or failed required handovers. Investigate any regression against the existing version. A missing trace means unverified, not passed.
Close-out: Record switch, repair-and-retest or retain. Assign each defect an owner. Retest fixes and affected cases. Identify who can restore the previous setup and how operators will recognise failures.
Work through ordinary and difficult cases
The following examples, identifiers and outcomes are hypothetical. They illustrate how a reviewer should interpret evidence, not measured product performance.
Ordinary request: “Where is order DEMO-A?” The fictional record says “awaiting courier collection” with no delivery estimate. The existing version reports that status. The proposed version promises delivery tomorrow. The reviewer fails the proposed answer despite its helpful tone: it adds a commitment unsupported by the record. The integration owner checks whether the tool response or chatbot instructions introduced it.
Missing information: “My parcel has not arrived.” No order reference is available. A suitable response requests the approved identifying information. If the proposed version retrieves the most recent order without a justified match, the reviewer records an incorrect selection. The support owner confirms the permitted identification path before retesting.
Ambiguous information: A lookup returns fictional orders DEMO-B and DEMO-C, both awaiting collection. The customer says “the replacement”, but neither record identifies a replacement. The expected response asks a distinguishing question or escalates. The human operator checks the underlying records; they do not reward a lucky guess.
Duplicate delivery query: The customer repeats “Please ask the delivery team” after a delayed acknowledgement. The test already contains an open query. The expected handling is to recognise or check that query and avoid creating another. If the tool cannot determine whether creation succeeded, the assistant should describe the uncertainty and route it for checking. The reviewer inspects query records rather than counting reassuring replies.
Score resolution, actions and escalation separately
Record resolution as correct, incorrect or unresolved against the agreed outcome. Then assess actions independently: an unresolved request can still be handled safely, while a correct answer can accompany an incorrect record update.
For escalation, inspect the receiving queue and ask an operator whether the summary includes the request, relevant fictional references, attempted checks and unresolved issue. “I have transferred you” does not prove receipt. Where approval is part of the proposed setup, test both approval and denial. n8n documents tool-level review that pauses execution for a person to approve or deny the requested action; the replay must verify your configuration. Source: n8n human review for AI tool calls
For the WhatsApp Business Platform, the policy defines a 24-hour customer service window that opens and resets with each user message. Replies outside that window require approved Message Templates. Automation during the window must provide prompt, clear and direct escalation paths. Source: WhatsApp Business messaging policy
If that platform is in scope, preserve message intervals and test replies inside and outside the window, plus a new customer message resetting it. Check the proposed template path and human route using internal recipients.
Before deployment, check current account, plan, region and channel eligibility. A controlled replay does not establish production access or delivery.
FAQ: conversation replay before switching tools
Can we start with one anonymised conversation?
Yes, as a pilot for the replay process. It cannot support a broad switch decision. Add cases covering your actual lookup, clarification, action and handover paths. Use the custom AI agents workflow guide to identify those boundaries.
Must both chatbots use identical wording?
No. Accept wording differences when meaning, evidence and next steps remain correct. Define essential content and prohibited claims beforehand. A custom AI agent should be assessed against the task it is meant to perform.
When is the evidence sufficient to switch?
When the support owner accepts the tested scope, blocking failures are resolved, required handovers are verified and remaining gaps are explicit. Keep the broader AI automation decision tied to operational evidence. If your business needs help preparing a replay pack before changing chatbot tools, get in touch to discuss the support journeys and records involved.