Build the Right AI Agent for Your Business Workflow
Syntora can build AI agents designed for specific, multi-step workflows with clear inputs and outputs. These agents are effective for tasks like lead qualification, document processing, and customer support triage. The complexity of an AI agent system depends on the number of decision points and external tools involved in the workflow. A single-purpose agent that summarizes meeting transcripts is less complex than a multi-agent system coordinating several specialized sub-agents with human escalation points, which typically requires a more detailed architecture based on a state machine.
Syntora designs and engineers AI agents for specific, multi-step workflows. Our approach focuses on transparent architecture using state machines and specialized sub-agents. We build systems capable of automating tasks like document processing and lead qualification.
What Problem Does This Solve?
Many teams start building workflows with point-and-click automation tools. These platforms are great for simple, linear tasks but fail when state management and conditional logic get complex. A workflow that needs to check three systems before acting requires duplicated, branching paths that quickly become unmanageable and expensive due to task-based pricing.
A common next step is the OpenAI Assistants API. While powerful for conversation, it is not built for autonomous, multi-step business processes. The orchestration is a black box, making it nearly impossible to debug when a step fails. It lacks durable state management for workflows that might pause for hours, and you cannot easily integrate custom Python libraries for tasks like parsing complex PDF documents.
We saw this with a 12-person recruiting firm that tried to automate applicant screening. Their workflow would trigger on a new resume, send it to an AI for a summary, and post it to Slack. It broke constantly. It could not parse PDF tables correctly, check if the candidate was a duplicate in their ATS, or escalate low-confidence summaries to a human reviewer. They spent over $250 a month for a system that still required 90% manual review.
How Would Syntora Approach This?
Syntora's approach would begin by mapping your specific workflow onto a formal state machine, often using LangGraph. Each distinct step would become a node and each decision a conditional edge within a graph. This visual model helps ensure all possible paths are considered before engineering begins. For long-running processes that require state persistence, we would use a Supabase Postgres database to store the ongoing status of each workflow instance.
Within this framework, a supervisor agent, defined in Python, would direct tasks to specialized sub-agents. For instance, to process incoming documents like resumes, a PDF_Parser_Agent could use the PyMuPDF library to extract text and tables. A Deduplication_Agent could then run a SQL query against the Supabase database to identify existing records. A Claude_API_Agent, using models like Claude 3 Sonnet, would then summarize the document and extract key qualifications into a structured JSON object with an associated confidence score. Syntora has experience building similar document processing pipelines using Claude API for financial documents, and the same robust pattern applies to other document types.
The entire system would be packaged as a FastAPI application within a Docker container. We would deploy it to AWS Lambda using Mangum, which enables the web application to function in a serverless environment. Workflows could be triggered by events such as a webhook from an existing Applicant Tracking System like Greenhouse, establishing an event-driven architecture designed for high throughput.
We would implement structured logging with structlog for system observability. Error handling would be designed to automatically pause workflows under defined conditions, such as an agent failing repeatedly or a summary's confidence score falling below a threshold. In such cases, the system could use an API (e.g., Asana API) to create a task for a human reviewer, providing a direct link to the specific workflow state requiring attention. A typical engagement for designing and deploying a system of this complexity usually takes 3-4 weeks, starting from initial discovery and continuing through architecture, engineering, and deployment. Clients would typically need to provide access to relevant data sources and subject matter experts during the discovery phase.
What Are the Key Benefits?
Go from Idea to Production in 4 Weeks
Your custom agent system is live and processing real work in under a month. No lengthy implementation cycles or multi-quarter roadmaps.
Pay for Execution, Not Idle Time
Our serverless architecture means you only pay for compute time, often under $50/month. No fixed monthly SaaS fees that you pay regardless of usage.
You Own the Code and the Infrastructure
We deliver the complete source code in your private GitHub repository and deploy it in your AWS account. You have full control and ownership from day one.
Alerts Before Your Users Notice Problems
Built-in health checks and error monitoring alert us to issues like a failed API dependency. Workflows pause gracefully instead of failing silently.
Connects to Any System with an API
We build direct integrations to your tools like HubSpot, Greenhouse, or internal databases. No limitations from a pre-built connector library.
What Does the Process Look Like?
Week 1: Workflow Mapping & Access
You provide API keys for the necessary systems and walk us through the target workflow. We deliver a formal state machine diagram for your approval.
Weeks 2-3: Core System Build
We write the agent logic, orchestration layer, and integrations. You receive access to a staging environment to test the workflow with sample data.
Week 4: Deployment & Integration
We deploy the system to your production environment, connect the live webhooks, and jointly monitor the first 100 successful runs. Your system is now live.
Post-Launch: Monitoring & Handoff
We provide 30 days of included monitoring and support. At the end of the period, you receive a technical runbook and video walkthrough of the codebase.
Frequently Asked Questions
- How much does a custom agent system cost?
- Pricing is based on scope. The main factors are the number of integrated systems, the complexity of the decision logic, and any requirements for a human-in-the-loop interface. A simple two-system workflow typically takes 3 weeks to build. A more complex system connecting five APIs with human review steps may take 6 weeks or more. Book a discovery call to discuss your specific workflow and get a fixed-price proposal.
- What happens if an API the agent depends on goes down?
- We build in exponential backoff retry logic for all external API calls. If a call fails after three retries, the workflow's current state is saved to the Supabase database and an alert is sent. Once the external service is restored, the workflow can be resumed from the exact point of failure without losing any data or context. This ensures high reliability for business-critical processes.
- How is this different from using the OpenAI Assistants API?
- The Assistants API is a managed, black-box service ideal for chatbots. Syntora builds transparent, auditable systems for business processes using open libraries like LangGraph. You get full code ownership, direct control over the orchestration logic, and a persistent state machine in your own database. This allows for deeper customization, better debugging, and the ability to host the system anywhere you choose.
- Can these agents handle files like PDFs or spreadsheets?
- Yes. A common design is a dispatcher agent that first identifies the file type. It then routes PDFs to a sub-agent using a library like PyMuPDF for text extraction, and CSVs or Excel files to another sub-agent using Pandas for data processing. This modular approach makes it simple to add support for new file formats in the future without rebuilding the core orchestration logic.
- What kind of human-in-the-loop is possible?
- We can build escalation points at any step in the workflow. The most common method is creating a task in a tool like Asana or Jira with a link to the paused workflow. We can also build simple web UIs hosted on Vercel where a user can approve or reject a step, correct extracted data, or provide missing information via a form. The agent remains paused until it receives the human input.
- Do I need to maintain servers for this?
- No. We build and deploy on serverless platforms like AWS Lambda and Vercel. There are no servers to patch, manage, or pay for when the system is idle. You only pay for the exact computation used during a workflow run, which is typically fractions of a second. This keeps infrastructure costs extremely low and predictable, often under $50 per month for thousands of executions.
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