Build AI Agents to Automate Your Business-Critical Workflows
AI agents are software programs that autonomously complete multi-step tasks using language models. For small businesses, they handle complex workflows like lead qualification, document processing, and customer support.
Syntora engineers custom AI agents to automate complex, multi-step business workflows. For a marketing agency, Syntora developed a Python-based system that automates Google Ads campaign management, including bid optimization and reporting. Syntora offers similar engineering engagements to design and deploy tailored AI agent solutions for various industry-specific challenges.
This is not simple automation. It involves state management, conditional logic, and connecting multiple APIs to make decisions. The agent remembers what it has done and decides what to do next, handling ambiguity and errors without human intervention.
Syntora engineers custom AI agent systems. We have experience deploying automated workflows, such as a Python-based system for a marketing agency that manages Google Ads campaign creation, bid optimization, and performance reporting. We apply this engineering expertise to design and implement tailored AI agents that address your specific operational challenges, such as automating lead qualification processes.
What Problem Does This Solve?
Many businesses try to automate workflows with visual, point-to-point connectors. These tools are great for simple tasks like 'when a form is submitted, create a CRM record.' They fail when the workflow requires logic, memory, or multiple steps that depend on each other. Their error handling is often just 'stop and send an email,' which leaves you with a broken process and a full inbox.
A typical scenario is lead processing for a B2B service firm. A lead fills out a form. A human then manually checks the CRM for duplicates, searches LinkedIn for company size, cross-references a Google Sheet of target accounts, and routes the lead to the right person in Slack. This takes 15 minutes of skilled work for every single lead, which at 20 leads per day is over 3 hours of manual effort.
A visual workflow builder can't replicate this. It can trigger on the form fill, but it cannot dynamically decide to search LinkedIn only if the email address is not a free provider. It cannot merge a new lead with an existing CRM contact based on custom rules. The result is a brittle chain of tasks that breaks silently and cannot handle the complexity of real business logic.
How Would Syntora Approach This?
Syntora begins each engagement by understanding your specific workflow and business rules. We would then design a custom system, mapping your workflow into a state machine using Python and LangGraph. This architecture frames each distinct step, such as enriching a lead, checking a CRM, or routing to a sales representative, as an independently testable node. This design allows the agent to manage loops, retry failed steps, and orchestrate specialized sub-agents. All process state would be persisted in a Supabase Postgres database, ensuring that even multi-step processes can pause and resume without losing context.
To handle distinct tasks, Syntora would build specialized sub-agents using APIs like the Claude API. For example, one sub-agent might take a company domain to find firmographic data, while another queries HubSpot to identify and merge duplicate contacts. A supervisor agent would then orchestrate these sub-agents, passing information and deciding the path based on the results. This modular approach ensures the system is maintainable and adaptable as your needs evolve.
Deployment for such a system would typically involve a FastAPI application on AWS Lambda, triggered by a webhook from your website form or another event source. When a new lead arrives, the webhook would initiate the supervisor agent's workflow. This serverless architecture is designed for scalability and cost efficiency, adapting to your operational volume.
Syntora's design includes robust error handling and a human-in-the-loop escalation path. If an agent encounters an unhandled situation, it would not simply fail. Instead, it would package its current state, a summary of its actions, and its recommended next step into a detailed notification. A human operator could then review, approve, or redirect the agent, ensuring business continuity while also providing data to refine the system for future edge cases.
What Are the Key Benefits?
From Workflow Map to Live Agent in 4 Weeks
We deploy a production-ready system in under 20 business days. You see results fast, without a lengthy development cycle or internal team distraction.
Pay for Execution, Not for Seats or Tasks
Your system runs on AWS Lambda. You pay for compute seconds, not per-user licenses or arbitrary task counts. This decouples your cost from your team size.
You Get the Keys and the Blueprints
We deliver the complete Python source code in your private GitHub repository, along with deployment scripts and a runbook. You have full ownership and control.
Know About Errors Before Your Customers Do
We configure structured logging with structlog and send alerts to Slack for any processing failures or API errors. The system self-reports problems instead of failing silently.
Connects Any API, Not Just Pre-Built Apps
Your agents can talk to your internal databases, proprietary software, or any third-party service with an API. We write the custom connection code using httpx.
What Does the Process Look Like?
Workflow Discovery (Week 1)
You provide access to your existing tools and walk me through the current manual process. The deliverable is a detailed state diagram of the proposed agent workflow.
Core Agent Build (Weeks 2-3)
I build the supervisor and sub-agents in a development environment. You receive a video demo of the agent processing a test lead from start to finish.
Integration and Deployment (Week 4)
I deploy the system to AWS Lambda and connect the live webhooks. You receive admin credentials and we process the first 20 real leads together.
Monitoring and Handoff (Weeks 5-8)
I monitor the system in production, tune performance, and handle any edge cases. At the end of week 8, you receive the final runbook and source code transfer.
Frequently Asked Questions
- How much does a custom AI agent system cost?
- Pricing depends on the number of steps in the workflow and the number of external APIs we need to integrate. A lead qualification agent connecting a web form, a CRM, and an enrichment API is a standard build. A document processing agent that needs OCR and custom data extraction is more complex. Book a discovery call to discuss scope and get a fixed-price quote.
- What happens when an external API like a CRM is down?
- The agent is built with exponential backoff and retry logic. It will try to call the API 3 times over 5 minutes. If it still fails, it logs the error, marks the lead as 'pending CRM sync', and sends a non-urgent Slack alert. The process never halts or loses data due to a single downstream service failure.
- How is this different from hiring a freelance developer on Upwork?
- A freelance developer can write Python scripts. I build, deploy, and maintain production-grade systems with orchestration, state management, and human-in-the-loop escalation. You are not hiring a coder; you are hiring an engineer who owns the entire lifecycle from architecture to monitoring. The person you talk to on the discovery call is the person who writes every line of code.
- Can these agents handle tasks that require judgment, like writing emails?
- Yes. We use the Claude 3 Opus model for tasks requiring nuanced language understanding or generation. For example, an agent can triage a support ticket and then draft a personalized reply based on the customer's history. This draft is sent to a human for one-click approval before sending, combining AI speed with human oversight.
- What kind of access do you need to our systems?
- I need API keys or service account credentials with the minimum required permissions. For a CRM, this is typically read access to contacts and write access to create new ones and update specific fields. I never need admin-level access or user logins. All credentials are stored securely in AWS Secrets Manager, not in the code.
- What is the ongoing maintenance commitment for us?
- For the first 8 weeks post-launch, I handle all maintenance. After that, the system is designed to be low-touch. You will only need to intervene if an external API you use changes its authentication method. I provide an optional monthly retainer for ongoing support, which covers proactive monitoring and up to 5 hours of development.
Related Solutions
Ready to Automate Your Technology Operations?
Book a call to discuss how we can implement ai automation for your technology business.
Book a Call