Automatically route incoming requests to the right AI agent based on confidence, with a human fallback when needed.
Classifies incoming requests as simple or complex using an AI supervisor. Returns a confidence score with reasoning. For high-confidence tasks, the executor AI handles the task; for low-confidence items, a manual email fallback is triggered to ensure accuracy.
Routes tasks to the right executor based on confidence and task type.
Classify requests as simple or complex.
Compute and return a confidence score with reasoning.
Route high-confidence tasks to the appropriate executor agent.
Choose Simple Agent Tool for basic tasks or Complex Agent Tool for advanced tasks.
Execute the task using the designated AI agent.
Trigger a low-confidence alert via email for human review.
Before the AI agent was deployed, routing was manual and error-prone. After deployment, routing is automated with reliable outcomes.
A simple 3-step flow is easy for non-technical teams to follow.
The incoming user request is captured by a webhook and stored with metadata for processing.
The Supervisor AI analyzes the request, classifies it as simple or complex, and returns a confidence score plus reasoning.
If the score meets the threshold, the Executor AI is invoked and routes to the Simple Agent Tool or Complex Agent Tool; if not, a fallback email alert is sent for human review.
A practical scenario showing end-to-end routing and outcome.
Scenario: A user requests a password reset via chat. The Supervisor AI classifies it as simple with high confidence. The Executor AI uses the Simple Agent Tool to reset the password in under 60 seconds. The task completes automatically with a successful outcome and no human intervention required.
One supporting sentence.
Streamlines triage and routing to automation paths.
Gains visibility into decisions and SLA compliance.
Ensures consistent task handling with auditable logs.
Easily connects multiple AI agents and tools.
Reduces escalations while maintaining safety.
Maintains control with fallback safety and review.
One supporting sentence with short explanation.
Underpins Supervisor, Executor, and AI agents to process tasks and generate responses.
Captures incoming requests and triggers the AI agent flow.
Sends manual-review alerts when confidence is low.
Delivers fallback notifications to the reviewer.
Orchestrates routing between Supervisor, Executor, and agents.
Six practical scenarios showing concrete outcomes.
One supporting sentence with short explanation.
When confidence falls below the threshold, the flow halts automated execution and triggers a manual review via email. The reviewer receives task details, the confidence score, and suggested actions. The user who submitted the request may be notified that human review is in progress. Reviewers can approve, modify, or reject the proposed action, after which the system can re-run or escalate. This safeguards against incorrect automation and preserves reliability.
Yes. You can adjust the confidenceThreshold value in the configuration node and test across boundary cases. It is recommended to run parallel tests with varying request types to validate the new threshold before going live.
Tasks that fit the Simple Agent Tool can be automated end-to-end. Complex tasks requiring specialized actions route to the Complex Agent Tool. If the request is ambiguous or high-risk, the system flags it for human review to maintain accuracy and safety.
Access is restricted to authorized components and users. All task inputs and results flow through secure channels, with audit logs for decisions. Email fallbacks include recipient controls to prevent leakage, and sensitive prompts are kept within controlled environments.
Yes. It integrates with OpenAI, webhooks, email services, and workflow orchestrators like n8n. Prompts and routing rules can be customized to align with your ticketing or CRM workflows, enabling a smooth integration path.
Yes. The architecture gates automated execution with confidence scoring, minimizing unnecessary long-running automation. High-volume requests are handled by parallelized agent tools, while rare low-confidence cases are escalated through the fallback channel without overloading agents.
Humans intervene only when confidence is low. Reviewers can approve, modify, or reject automated actions, after which the flow can re-run with updated inputs or escalate as needed. This keeps automation safe while preserving the ability to handle edge cases.
Automatically route incoming requests to the right AI agent based on confidence, with a human fallback when needed.
