Make Introduces Real-Time Chat Interface for Testing AI Agents During Development.

Make has launched a chat-based testing interface for its AI Agents, allowing developers to interact with their agents in real time while building automation workflows. The feature, announced on LinkedIn, is designed to speed up development cycles by enabling builders to spot issues and refine agent logic without executing the entire workflow.

The chat interface appears directly within Make's visual scenario builder, where users construct automation workflows. Builders can now test how an agent responds to different inputs, validate decision logic, and adjust behavior on the fly. The approach addresses a common bottleneck in agent development: the need to redeploy or rerun lengthy workflows just to verify a single change.

Traditional workflow testing requires triggering the entire automation sequence, which can be time-consuming when workflows involve multiple steps or external API calls. Make's chat interface isolates the agent's decision-making layer, letting developers query it directly. A builder working on a customer support agent, for example, could ask test questions and immediately see how the agent would route or respond, without waiting for upstream data processing steps to complete.

This method reduces the feedback loop from minutes to seconds. Developers can iterate on prompts, adjust conditional logic, or tune response patterns while the agent is still in draft state. The feature is available across all Make plans, according to the product page.

The chat feature builds on Make's recent push to make AI Agents more transparent and easier to deploy. The company has positioned its agents as tools that combine adaptive AI decision-making with the reliability of traditional automation. Agents in Make can orchestrate tasks across more than 3,000 app integrations, handling everything from data routing to conditional workflows.

Make has also rolled out an Agent Library with pre-built templates and enabled public sharing of agent scenarios, allowing teams to duplicate and customize workflows. The chat interface fits into this ecosystem as a debugging and refinement tool, rather than a standalone feature.

For teams working with agents that need to handle edge cases or nuanced decision trees, the ability to probe agent behavior conversationally could reduce trial-and-error cycles. It also provides a clearer view into how the agent interprets instructions, which is useful for non-technical stakeholders who need to validate outputs before production deployment.

The chat interface is most useful for agents that handle dynamic or unstructured inputs. A customer service agent routing tickets based on sentiment or urgency could be tested with dozens of sample messages in rapid succession. A sales lead qualifier could be validated against different prospect profiles without needing real CRM data.

Developers building agents that rely on external APIs or large language models can use the chat to verify that prompts are structured correctly and that responses align with business rules. Since the chat operates within the scenario builder, any adjustments made during testing can be immediately saved to the workflow.

Make has not disclosed whether the chat interface logs conversations for later analysis or whether it supports batch testing scenarios. The feature appears to be focused on interactive, real-time validation rather than automated testing workflows.

The update arrives as automation platforms increasingly add AI capabilities. Zapier and n8n have both introduced agent-like features, but Make's integration of chat-based testing directly into the builder differentiates its approach. The company is emphasizing speed and transparency as it competes in a crowded market where AI and automation are converging.

For more details, visit the Make AI Agents product page.