Automate Your Business Operations with Custom AI Agents

Many businesses attempt automation with linear, webhook-based platforms. These tools are excellent for simple A-to-B connections, but they fail when a process requires stateful, multi-step coordination. They charge per task, so a single workflow that checks inventory, verifies customer credit, and generates an invoice can burn through 5-10 tasks, making high-volume automation expensive.

A common failure point occurs with out-of-order events. Consider a client onboarding workflow: it needs a signed contract from DocuSign and a paid invoice from Stripe before creating a project in Asana. If the client pays the invoice before signing the contract, a linear workflow breaks. It cannot hold the 'paid' state while waiting for the 'signed' state. Teams are forced to build brittle, branching paths that are impossible to debug and maintain.

Simple AI wrappers face a different problem. Connecting a large language model to an API is easy, but it is not a reliable system. These models hallucinate function calls, fail to handle API errors gracefully, and have no long-term memory of a workflow's state. A prototype might work for a demo, but it cannot be trusted to run a core business operation autonomously.

Syntora would approach your operational challenge by first conducting a discovery phase to map your business operation as a state machine. Each step, such as 'awaiting_payment' or 'project_created', would become a defined state in a Supabase Postgres database. This persistence layer serves as the agent's memory, allowing a workflow to pause for days and resume exactly where it left off once a trigger, like a webhook from Stripe, is received.

Using this state map, we would design a multi-agent system in Python with LangGraph. A central supervisor agent would coordinate specialized sub-agents. For example, in a client onboarding process, one sub-agent could exclusively handle the DocuSign API, another manage Stripe, and a third provision projects in Asana. Any step requiring language understanding, such as extracting a project name from an email, would be handled by a call to the Claude API with structured JSON output. We have experience building similar structured extraction pipelines.

The core of this system would be packaged as a FastAPI application and deployed on AWS Lambda. This serverless architecture ensures that you only incur compute costs when a workflow is active. When a webhook hits the FastAPI endpoint, it would trigger the Lambda function, which would execute the appropriate agent. This design is chosen for its efficiency and reliability compared to polled triggers.

We would implement structured logging using `structlog` for every state transition and API call. If a sub-agent were to fail to connect to an external service after a set number of automated retries, the supervisor agent would pause the workflow. It would then send a detailed alert to a designated Slack channel with a direct link to the workflow's record in Supabase, enabling human-in-the-loop escalation to resolve the issue and resume the process. This ensures operational resilience and provides transparency into agent actions.