Code Review Checklist for AI Chatbot Agencies

Review how client IDs are passed through services, queues, vector stores, and webhook handlers to ensure one client bot can never access another client's prompts, files, or chat history. For agencies managing many brands, even a single missing tenant filter can create a major trust and compliance incident.

Confirm API keys, webhook secrets, CRM tokens, and LLM credentials are loaded per client workspace rather than from shared defaults. This prevents accidental cross-client routing when cloning a bot for a new onboarding.

Inspect how premium features, channel support, and custom actions are toggled for each client. Agencies often reuse codebases, so feature flags need safe defaults and explicit checks to avoid exposing unfinished capabilities to the wrong account.

Look for duplicated flows, prompt files, or connector logic across client projects and recommend shared modules where appropriate. This reduces maintenance overhead when updating intent routing, safety rules, or response formatting across dozens of bots.

Review tables and collections for tenant-aware indexing, client-level usage tracking, and efficient querying by workspace, channel, and assistant instance. Poor schema design becomes expensive once the agency manages high message volume across many retainers.

Check whether inbound Telegram, Discord, website chat, or CRM events are validated against the correct client configuration before processing. This is especially important when agencies white-label bots and maintain multiple domains or channel tokens under one backend.

Make sure system prompts, role instructions, and fallback responses reflect each client's tone, escalation policy, service area, and prohibited claims. Agencies often templatize prompts, but missing brand-specific constraints can lead to off-message replies in production.

Inspect how the bot treats untrusted user messages, uploaded documents, and retrieved knowledge base text before adding them to the model context. Reviews should flag missing guardrails that allow users to override system instructions or expose hidden prompts.

Review whether actions like booking appointments, updating CRM records, or sending emails are restricted to the correct bot states and client-approved tools. This matters when agencies support multiple industries with different risk levels and approval rules.

Check chunking rules, metadata filters, and ranking thresholds so the assistant only pulls current documents tied to the right client and service line. Weak retrieval causes hallucinations that are hard to diagnose during client QA.

Look for oversized prompts, repeated instructions, or unnecessary transcript history that increases cost and latency. Agencies with usage-based billing need code reviews to catch waste before it erodes client margins.

Review how the assistant responds when retrieval fails, tool calls error, or confidence scores drop below threshold. The fallback should preserve the client's brand voice while moving the conversation toward clarification or human escalation.

Review logging middleware to ensure phone numbers, emails, access tokens, payment references, and protected client data are masked before being sent to observability tools. Agencies debugging across many tenants need logs, but not at the expense of client confidentiality.

Inspect admin panels, support tools, and review environments to ensure team members only see the clients they manage. This is especially important for agencies with account managers, contractors, and developers working in parallel.

Confirm the code supports client-specific retention windows, deletion requests, and archival rules for chat logs and vectorized content. Different industries have different compliance expectations, so retention should be configurable rather than hard-coded.

Check whether CRM, calendar, help desk, and messaging credentials are encrypted at rest and never embedded in source files. Code review should also flag manual secret sharing practices that make client offboarding risky.

Review whether tool executions such as contact creation, booking changes, or ticket updates record who triggered the action, which prompt led to it, and which tenant was affected. This creates accountability when clients question an automated action.

If bots operate in healthcare, finance, legal intake, or other sensitive sectors, review whether the code reliably shows required disclosures before collecting information or triggering AI-generated recommendations. Agencies serving multiple verticals should not assume one disclaimer fits all.

Make sure test coverage includes onboarding flows, lead qualification branches, handoff logic, and channel-specific formatting for each major bot template. Generic unit tests are not enough when agencies promise custom behavior per client.

When prompts, embeddings, or knowledge sources change, review whether the team runs scenario-based evaluations against expected outcomes. This helps agencies catch subtle behavior drift before a routine client update damages conversion rates.

Inspect retry logic, circuit breakers, timeout handling, and user-facing fallback messages for model providers, CRMs, calendars, and messaging APIs. Client bots should degrade gracefully rather than freezing in the middle of a lead capture or support flow.

Review release workflows to ensure one failed client customization does not require rolling back every bot on the platform. Agencies need the ability to isolate bad deployments quickly when multiple retainers depend on the same codebase.

Check how inbound bursts from campaigns, launches, or seasonal traffic are handled across tenants so one client's spike does not starve others. This is especially relevant for agencies running many promotional bots at the same time.

Ensure alerts cover failed webhooks, elevated latency, tool-call errors, rising token costs, and unusual fallback rates at the client level. Agency teams need enough granularity to spot an issue before the client notices degraded performance.

Review whether staging uses realistic channel connectors, knowledge base structures, and permission scopes rather than mock-only setups. Many chatbot defects appear only when real Telegram, Discord, CRM, or booking integrations are exercised end to end.

Review how token usage, message volume, tool executions, and API calls are tagged so agencies can bill accurately by retainer, overage, or white-label plan. Misattribution quickly turns profitable accounts into support headaches.

Inspect billing code for edge cases such as prorated launches, paused bots, included usage thresholds, and custom enterprise pricing. Agencies often negotiate unique deals, so billing rules should be explicit and testable.

Make sure dashboards and monthly reports pull from validated metrics for leads captured, resolution rate, human handoffs, and AI spend. Agencies rely on these reports to justify renewals, so data mismatches can undermine trust quickly.

Check whether logos, domains, sender names, support links, and branding strings are pulled from client settings instead of hard-coded values. This reduces turnaround time when onboarding or rebranding agency clients.

Review whether code supports clean export of transcripts, prompts, and analytics while revoking tokens and deleting tenant data according to contract terms. Agencies need smooth offboarding to reduce legal and operational friction.

Make sure each client bot has readable documentation for prompt versions, integrations, billing rules, and escalation settings so account managers and developers can step in without guesswork. This is essential for agencies juggling many active accounts.