Feature Request

Request a Feature for the respond.io platform.
MCP: To create and manage Workflows
Business Problem Businesses often know the automation outcome they want but still need to manually configure every Workflow through the UI. Creating triggers, connecting steps, setting up branches, validating the graph, testing different paths, and publishing safely can be time-consuming and requires detailed knowledge of the Workflow builder. Without agent-assisted Workflow creation and validation, users may create incomplete configurations, miss required connections, introduce unintended side effects during testing, or publish changes without a clear understanding of their impact. Managing existing Draft or Stopped Workflows also requires users to inspect and troubleshoot them manually. Desired Outcome Enable an AI agent to turn an approved automation goal into a working Workflow through a controlled, permission-aware process. The agent should be able to: Propose and create a Draft Workflow using supported Triggers, Steps, Branches, and connections. Inspect and edit Draft or Stopped Workflows while respecting user permissions and protecting existing versions. Validate the complete Workflow graph and return actionable errors, warnings, and publish blockers. Test representative paths using the supported test mechanism, with side effects isolated where the platform allows. Present a clear publish summary and require explicit user confirmation before publishing. Publish an attributable Workflow version, inspect authoritative run status, and support eligible retries or recovery to a previous version where the platform supports it. Use Cases An onboarding specialist describes a business goal, such as qualifying new leads and assigning them to the right team, and the agent creates a Draft Workflow for review. A user asks the agent to inspect a Stopped Workflow, identify the failure, and propose or apply a permitted fix without overwriting the existing version. A user validates a Workflow before publishing and receives clear information about missing connections, invalid configurations, warnings, and blockers. A user tests representative branches without triggering unintended customer messages or other side effects where isolation is supported. A team reviews the proposed changes, affected paths, and expected behavior before explicitly approving publication. After publishing, users can identify which agent or user created the version, inspect the authoritative run status, retry eligible runs, or recover a previous version where supported.
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AI Copilot: Brainstorming
Business Problem When users ask Copilot to complete a task, they may not know which decisions or constraints they need to specify. Instead of surfacing these important gaps, Copilot may fill them with assumptions. This can produce an outcome that appears complete but does not match what the user actually needs. For example, when asked to create a Support AI Agent, Copilot may assume what the Agent should handle or when it should hand off. The user may only discover these mismatches when reviewing or testing the proposed setup. Desired Outcome Help Copilot turn an incomplete request into an agreed plan before proceeding. Copilot should: Ask clarifying questions with varying depth: Surface important gaps based on the request’s ambiguity, effort and potential impact. Provide a recommendation: Recommend an approach based on the user’s answers and explain important decisions or trade-offs. Provide a plan before execution: Summarize the intended outcome and proposed changes for the user to review and refine. Copilot should provide useful direction quickly. Straightforward questions and bounded changes should proceed without unnecessary planning. Use Cases A user asks Copilot to create a Support AI Agent. Copilot clarifies its responsibilities, Knowledge Sources and handoff rules before recommending and presenting the setup plan. A user asks how to create a Workflow for routing high-value leads. Copilot clarifies the qualification criteria, routing destination and exceptions before recommending a Workflow structure and explaining how to configure it. Current Workaround Users must anticipate which details matter and provide them upfront. Otherwise, they must identify unsuitable assumptions during review or testing and correct them through repeated messages.
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AI Agents: Prioritize Knowledge Sources by File Name
Business Problem: Right now, users lack direct control over which Knowledge Source an AI Agent should prioritize when answering contacts. The AI Agent searches across all available Knowledge Sources and picks what seems most relevant. This can cause problems when multiple sources contain similar information. For example, if a workspace has both a current Pricing Guide and an old marketing PDF that mentions prices, the AI Agent may use the wrong source. This can lead to incorrect or outdated answers. There is no direct setting for admins to tell the AI Agent which Knowledge Source should be preferred for a certain topic, keyword, or type of question. Desired Outcome: Let admins influence which Knowledge Source the AI Agent should check first when answering contacts. Admins should be able to prioritize Knowledge Sources based on signals such as: Topic of the question, such as billing, pricing, refunds, or API support Keywords in the contact’s message, such as “refund” or “pricing” Source priority when multiple sources contain similar information If no priority rule or matching signal applies, the AI Agent should still search for the answer across all Knowledge Sources. Use Cases: A customer asks about prices. The AI Agent uses the current Pricing Guide instead of an old marketing PDF. A customer asks about refunds. The AI Agent checks the Refund Policy before general help articles. Current Workaround: Update the AI Agent Instructions to search for specific keywords when handling specific types of questions. Example: “For pricing questions, search for the keyword ‘Pricing Options 2026’." This may work for one or two topics, but it is not manageable when there are many topics.
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