Distribute Your AI Agents as Production-Ready Services
AI agents are distributed as hosted API endpoints that other systems call via webhooks. They are sold as custom-built systems for a flat fee, not as per-seat SaaS products.
Syntora specializes in designing and building AI agent distribution systems, allowing companies to sell or integrate custom-built AI agents via secure API endpoints. Their approach focuses on reliable state management, scalable serverless deployment, and robust observability for AI agent workflows. Syntora helps clients define, architect, and deploy custom AI agent solutions as a service engagement.
This approach works for agents triggered by real-time events, like a new CRM entry, or for agents that run on a schedule, like a daily compliance check. The agent's logic is wrapped in a private API, making it a secure, callable service that can be integrated into any existing software stack without exposing the core code or prompts.
Building and distributing an AI agent involves defining its specific purpose, data sources, and operational requirements. Syntora engineers would work with your team to establish the agent's expected behavior and integration points. Factors like the complexity of the agent's logic, the volume of data it processes, and the necessary integration with existing systems will determine the scope and timeline of the engagement. We have experience building similar document processing pipelines and API integrations in related financial domains, and the technical patterns are directly applicable.
What Problem Does This Solve?
A developer building an agent often gets a Python script working locally, but stalls when trying to make it a distributable product. The first instinct is to use a serverless platform, but this creates immediate limitations. A lead qualification agent that scrapes websites using Playwright will exceed the 50MB package size limit on Vercel or Netlify, failing at deployment.
Next, they might try a raw AWS Lambda function. This works for simple, stateless tasks. But if an agent needs to remember conversation history or manage a multi-step workflow, it fails. A standard Lambda function has a 15-minute timeout and no native state persistence. A long-running research task that takes 20 minutes will be killed mid-process, losing all its work.
Frustrated, some turn to a full virtual server on DigitalOcean or AWS EC2. Now, instead of building agents, they are managing Linux patches, configuring Nginx, and writing systemd services to keep the script from crashing. This approach is expensive for workflows that run intermittently, as you pay a fixed monthly fee for a server that sits idle 99% of the time.
How Would Syntora Approach This?
Syntora would approach the problem by first defining the agent's specific trigger mechanism and its state machine. For event-driven agents, we would design a FastAPI application exposing a secure webhook endpoint. For scheduled tasks, we would configure an Amazon EventBridge cron rule. The agent's workflow would be mapped using LangGraph, enabling complex loops, conditional logic, and human-in-the-loop escalation points for tasks requiring review.
State management is critical for agent reliability. All agent state would be managed externally in a Supabase Postgres database. When an agent needs to pause, await an API response, or resume after a failure, its current state would be persisted to a dedicated table. This pattern allows for robust workflows that can run for extended periods, addressing the stateless nature of many serverless environments.
The agent system would be packaged into a Docker container. For short-lived tasks, this container would be deployed to AWS Lambda, optimizing for cost and scalability at moderate volumes. For agents requiring longer execution times (e.g., beyond 15 minutes), AWS Fargate would be used. API access would be secured with keys managed in AWS Secrets Manager.
We prioritize observability from the outset. Syntora would implement structured logging using `structlog`, with logs directed to AWS CloudWatch. Alerts would be configured to trigger on high error rates or functions exceeding defined execution durations. A typical engagement includes an initial discovery phase, architecture design, development, and a stabilization period. Deliverables would include the deployed system, all infrastructure-as-code (Terraform) files, and a comprehensive runbook for ongoing maintenance. A typical build for this complexity would take 8-12 weeks, depending on the agent's specific logic and integration requirements. The client would need to provide access to relevant data sources, existing APIs, and subject matter expertise.
What Are the Key Benefits?
From Python Script to Production API in 2 Weeks
We turn your local agent script into a production-grade, webhook-driven service with logging and retries in 10 business days.
Pay for Execution, Not for Idle Servers
Using AWS Lambda, your agent only incurs costs when it runs. A workflow that runs 5,000 times a month often costs less than $30 in compute.
You Get the Keys and the Blueprints
We deliver the full source code in your private GitHub repository and the Terraform files for the infrastructure. You have zero vendor lock-in.
Alerts Fire Before Your Customers Complain
We integrate error tracking with Sentry and alerts with Slack. You know about a broken API key the moment it happens, not when a user reports it.
Connects to Anything with a Webhook
Your agent becomes a callable endpoint. Trigger it from HubSpot workflows, a new row in Google Sheets, or a Stripe payment event.
What Does the Process Look Like?
Scoping and Architecture (Week 1)
You provide the agent's core logic or spec and access to any required APIs. We design the state machine, define data models, and deliver an architecture diagram.
Core System Build (Week 2)
We build the FastAPI application, the LangGraph orchestration, and the Supabase persistence layer. You receive a link to the private GitHub repo to track progress.
Deployment and Testing (Week 3)
We deploy the system to a staging environment on AWS. You receive API documentation and a test webhook URL to validate the end-to-end workflow.
Production Handoff and Monitoring (Week 4)
After approval, we deploy to production. We monitor for 2 weeks, then hand over a runbook with instructions for maintenance and common failure modes.
Frequently Asked Questions
- How much does it cost to productionize an existing AI agent script?
- Pricing depends on three factors: the number of steps in the workflow, the number of external APIs it calls, and state management needs. A simple, stateless agent can be deployed in two weeks. A complex agent requiring a persistent database and human-in-the-loop escalation takes four to six weeks. We provide a fixed-price quote after our discovery call.
- What happens if the Claude API is down or returns an error?
- We build in exponential backoff and retry logic for all external API calls. If an API call fails three times, the agent's state is saved to the database and the task is moved to a dead-letter queue. A Slack alert is sent with the failed task ID, allowing for manual review and replay without losing the entire workflow's progress.
- How is this different from using a platform like Modal Labs?
- Modal Labs is a powerful platform but requires your team to manage Python environments and deployment configurations. Syntora delivers a fully managed, production-ready system with monitoring and a handoff runbook. We handle the infrastructure so you can focus on the agent's business logic, not on YAML files and Dockerfiles.
- Can I sell the agent you build to my own customers?
- Yes. We build the system for you, and you own the IP. The API we deliver can be integrated into your own SaaS product. We can architect it to support multiple tenants, where each of your customers gets their own secure API key and their data is isolated in the database. This is a common requirement.
- What if the agent's logic needs to be updated after handoff?
- You have the full source code in GitHub. We set up a CI/CD pipeline with GitHub Actions, so pushing a change to the main branch automatically triggers a new deployment. The runbook we provide includes instructions on how to create a staging environment to test changes before they go to production.
- Do you support agents that need to run in a browser?
- Yes. For agents that need to perform browser-based actions like scraping or filling forms, we use libraries like Playwright. We package the entire browser environment into the Docker container deployed on AWS Fargate, as this requires more resources than a standard Lambda function can provide. This allows the agent to operate with a real browser context.
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