Sentie +
Make (Integromat)

Make Builds the Scenarios. Sentie Adds the Judgment.

Make is one of the most powerful visual automation platforms available. Its scenario builder, module library, and data mapping capabilities let you construct sophisticated workflows that would require custom code on other platforms. For teams that need automation beyond simple trigger-action pairs, Make provides the flexibility to build virtually anything.

But flexibility creates its own challenges. Complex scenarios with dozens of modules, multiple branches, and intricate data transformations become difficult to maintain and debug. When a scenario fails partway through, understanding what went wrong and recovering gracefully requires expertise that not every team member has. And when a workflow requires a judgment call, like determining whether an incoming request is urgent, deciding which team should handle an edge case, or interpreting unstructured data, the visual builder hits its limits.

Sentie agents add an intelligence layer to your Make workflows. They handle the decisions that can't be expressed as filters and routers. They manage error recovery that goes beyond retry logic. They orchestrate complex multi-scenario processes as coherent business workflows rather than independent automation chains. Your existing scenarios continue to run exactly as they do today. Sentie enhances them with the cognitive capabilities that rule-based automation fundamentally lacks.

This combination is particularly powerful because Make's deep module library provides connectivity to virtually any tool, while Sentie's AI agents provide the reasoning capability that makes that connectivity truly useful. You get the breadth of Make's integrations with the intelligence of AI-powered decision-making.

AI-Powered Decision Points in Your Scenarios

Every complex automation eventually hits a point where the right path forward depends on judgment rather than rules. A customer request arrives that could be handled by sales, support, or account management depending on the content and context. A data import contains records that need to be categorized, but the categories aren't always clear from the field values. An approval request should be expedited or routed through full review based on factors that are hard to encode as filter conditions.

In Make's native toolset, these decision points are handled through filters, routers, and sometimes custom code modules. These work when the criteria are explicit and consistent. But when the decision depends on interpreting natural language, weighing multiple ambiguous factors, or applying business judgment that varies by context, rule-based branching produces either oversimplified routing or unmaintainably complex scenario trees.

Sentie agents serve as intelligent decision modules within your scenarios. At any branch point, you can route data through a Sentie agent that evaluates the situation using natural language understanding, contextual data from connected systems, and the business logic you've defined in plain language rather than filter conditions.

The agent returns a structured decision that your scenario's subsequent modules can act on. A customer message is classified as sales inquiry, support request, or account question with a confidence score and the reasoning behind the classification. A data record is categorized based on content analysis rather than keyword matching. An approval request is assessed for risk level based on multiple factors that would require dozens of filter branches to replicate.

These decision points improve over time. The agents learn from corrections and evolving business context, which means your scenarios adapt to changing conditions without manual reconfiguration of filter logic.

Intelligent Error Handling and Scenario Recovery

Anyone who has built complex Make scenarios knows the challenge of error handling. A module fails because an API returned an unexpected response. A data transformation produces a null value that breaks a downstream module. A rate limit forces a pause that cascades through dependent scenarios. Make's built-in error handling routes provide a foundation, but the response options are limited: retry, ignore, commit, rollback, or break.

The real challenge isn't handling the error itself. It's determining the right response and ensuring that the broader workflow recovers correctly. When a scenario fails at step 7 of 15, the first 6 steps have already executed. Downstream scenarios that depend on the output are waiting. The data state across your connected tools may be partially updated. Simple retry logic doesn't solve these problems.

Sentie's error handling agents bring contextual recovery to your scenarios. When a module fails, the agent evaluates the error type, the data involved, the current state of the workflow, and the impact on downstream processes. Based on this assessment, it selects the appropriate recovery strategy.

For transient errors like API timeouts or rate limits, the agent implements intelligent retry with appropriate backoff. For data errors, it attempts to normalize or correct the problematic data and reprocess. For dependency failures where a required resource isn't available yet, the agent queues the operation and monitors for the dependency to resolve. For structural errors that indicate a permanent problem, the agent logs a detailed diagnosis and alerts your team with enough context to fix the root cause.

The agents also manage cross-scenario recovery. When multiple scenarios are part of a larger business process, a failure in one needs to be communicated to the others. The agent coordinates this, pausing dependent scenarios, managing state across the process, and resuming everything in the correct order once recovery is complete. This turns fragile scenario chains into resilient business workflows.

Cross-Scenario Orchestration and Process Management

Real business processes span multiple Make scenarios. Customer onboarding might involve a scenario for CRM setup, another for project creation, another for billing configuration, and another for communication sequencing. Each scenario handles its piece, but coordinating the timing, dependencies, and data flow between them is left to the user. Make's scenario linking features help, but managing a ten-scenario business process with proper dependency tracking and state management is operationally demanding.

Sentie's orchestration agents manage these multi-scenario processes as unified workflows. You define the business process at a high level: the steps involved, the dependencies between them, the success criteria, and the failure handling rules. The agent coordinates the execution, ensuring scenarios run in the correct order, data flows properly between them, and the overall process completes successfully.

The orchestration layer provides visibility that individual scenarios can't. Instead of checking each scenario's execution log separately, you see the entire business process as a single timeline. If a step stalls, you see exactly where and why. If data needs to be corrected, you see the upstream source and the downstream impact. This process-level view makes managing complex automations dramatically simpler.

The agents also handle process variations intelligently. When the same business process needs to run differently based on the input, like a customer onboarding that varies by plan tier or region, the agent selects the appropriate scenario variants and adjusts the orchestration accordingly. You define the variations once, and the agent applies the right logic each time without requiring separate master scenarios for each variant.

For teams running dozens or hundreds of scenarios, this orchestration layer transforms Make from a collection of independent automations into a coherent automation platform where every scenario contributes to a managed, observable business process.

Scenario Analytics and Optimization

As your Make workspace grows, understanding how your automations perform, where they fail, and where they can be improved becomes increasingly difficult. Make's execution logs show what each scenario did, but analyzing patterns across scenarios, identifying bottlenecks, and understanding the business impact of automation performance requires analysis that the native tools don't provide.

Sentie's analytics agents provide continuous monitoring and optimization for your entire Make workspace. They track execution times, success rates, error frequencies, and data throughput across all your scenarios. When performance degrades, they identify the specific module or data pattern causing the slowdown. When error rates increase, they correlate the timing with changes in connected systems to pinpoint the root cause.

The agents also identify optimization opportunities. They detect scenarios that could be combined to reduce API calls and improve efficiency. They find redundant data transformations that different scenarios perform independently. They identify modules that consistently slow down execution and suggest alternatives. They flag scenarios that haven't run in months and might be candidates for cleanup.

For business impact tracking, the agents connect scenario execution to outcomes. How many leads were processed? How many orders were fulfilled? How many support tickets were resolved through automation? These outcome metrics tell you whether your automation investment is delivering returns, not just whether the scenarios executed successfully.

The analytics are delivered as regular reports with actionable recommendations. Rather than presenting raw execution data, the agents surface the insights that matter: "Your customer onboarding process takes 40% longer on average when the project creation step encounters a template error. Fixing the template validation in the third module would reduce onboarding time and prevent 12% of the manual interventions your team handles each week." The analysis is done. The opportunity is clear. Your team decides whether and when to act.

What You Can Automate