Build Autonomous Agent Systems with Claude AI
Multi-agent systems use a coordinator agent to assign sub-tasks to specialized worker agents. Each worker uses Claude AI's tool-use functions to operate a single application, like a calendar or CRM.
Syntora engineers multi-agent systems using Claude AI, designing specialized agents to automate complex team tasks. We build reliable, scalable architectures that integrate with existing applications and provide clear operational visibility.
Syntora designs and engineers these systems, with project scope depending on the number of integrated tools and the ambiguity of the workflow. For instance, a system connecting Google Calendar, Slack, and HubSpot often involves an initial build phase of 2-3 weeks. A more complex system that also needs to read unstructured PDFs from diverse sources requires additional logic for document understanding and robust parsing.
What Problem Does This Solve?
Many teams try to automate complex processes using linear workflow tools. A 12-person recruiting firm needs to screen candidates: read a resume PDF from Greenhouse, find a LinkedIn profile, score skills against a job description, and post a summary to Slack. A linear tool can trigger the first and last steps, but it cannot handle the ambiguous reasoning in the middle. It can only do simple keyword matching on the resume, which is unreliable.
Trying to solve this with a single, large prompt in a custom GPT wrapper also fails. Feeding a 5-page resume and a 2-page job description into one API call often leads to context window errors or hallucinations. The model gets confused by the volume of information and cannot reliably perform the multiple distinct steps of parsing, enriching, and scoring in a single pass.
These approaches fail because they treat a dynamic, multi-step process as a static, linear one. Real work requires planning and adaptation. A fixed workflow cannot recover when a resume is poorly formatted or a LinkedIn profile is missing. It breaks, requiring manual intervention that defeats the purpose of automation.
How Would Syntora Approach This?
Syntora begins by conducting a deep dive into the client's specific workflow to map out distinct roles and required tasks. For an illustrative scenario like automating parts of a recruiting process, this would involve designing a "Coordinator" agent, a "Resume-Parser" agent, and a "Profile-Enricher" agent. We would define the exact tools each agent can use via Anthropic's tool-use API schema. For instance, the Resume-Parser would be equipped with a `read_pdf` function built with the PyMuPDF library, and the Profile-Enricher would use a `search_linkedin` function calling the SerpApi service. Syntora has extensive experience building document processing pipelines using Claude API for complex financial documents, and the same architectural patterns apply here for diverse unstructured documents.
The agents would be built in Python using FastAPI as the API layer for modularity and scalability. The Coordinator agent would receive initial triggers, such as a webhook, and use Claude 3 Opus to break the high-level goal into a sequence of sub-tasks. It would then invoke the specialized worker agents. Each worker would typically run on the faster, more cost-effective Claude 3 Sonnet model to execute its single, defined function.
To manage system state and ensure workflow continuity, we would implement a Supabase Postgres table to track the status of each ongoing task. This design ensures the workflow can resume reliably even if a single agent temporarily encounters an issue. The entire system would be packaged and deployed as a serverless application on AWS Lambda, providing efficient scaling and cost management by paying only for compute time during execution.
Syntora would implement structured logging with `structlog`, sending detailed JSON logs of every agent action to AWS CloudWatch. We would then configure robust alarms that send real-time Slack notifications if, for example, the end-to-end processing time for a task exceeds a defined threshold or if any agent's error rate surpasses a critical percentage. This approach enables proactive performance monitoring and rapid troubleshooting, critical for maintaining operational stability.
A typical engagement with Syntora for a multi-agent system of this complexity involves an initial discovery phase to fully understand the client's existing processes and define clear success metrics. The client would typically need to provide access to relevant APIs, document examples, and subject matter expertise. Deliverables would include the deployed, production-ready system, comprehensive documentation, and knowledge transfer to the client's team for ongoing management.
What Are the Key Benefits?
First Results in 10 Business Days
We deploy a minimum viable agent system within two weeks. You see autonomous tasks completed by day 10, not after a three-month project plan.
Pay for Execution, Not Idle Servers
Serverless deployment on AWS Lambda means you only pay when the agents are working. Monthly hosting is often under $50, compared to hundreds for a dedicated server.
You Get The Python Source Code
At handoff, you receive the complete Python codebase in a private GitHub repository. You are not locked into a platform and can extend the system internally.
Alerts Before A Workflow Fails
We configure CloudWatch alarms for latency spikes and error rates. You get a Slack alert when something is slow, not after a customer complains.
Connects Natively to Your Tools
The agents use the same APIs your team uses. We build direct integrations to Greenhouse, HubSpot, Google Workspace, and any other system with a REST API.
What Does the Process Look Like?
Week 1: Scoping and Tool Audit
You provide read-only access to the relevant platforms (e.g., your CRM, email inbox). We document the exact workflow and define the agent roles and required API credentials.
Week 2: Core Agent Build
We build the agents and their tool-use functions in Python. You receive a daily video update showing progress and a link to the staging environment to see the agents run.
Week 3: Deployment and Integration
We deploy the system to AWS Lambda and connect it to your live applications via webhooks. We process the first 20-30 live tasks with you.
Week 4-8: Monitoring and Handoff
We monitor performance and error logs for one month post-launch. You receive a final runbook with system architecture diagrams and instructions for common maintenance tasks.
Frequently Asked Questions
- How much does a custom multi-agent system cost?
- Cost depends on the number of agents and the complexity of the tools they use. A two-agent system connecting two standard SaaS APIs is a smaller project. A four-agent system that needs to read unstructured documents and interact with a legacy database is more involved. After a 30-minute discovery call, we provide a fixed-price proposal.
- What happens when an agent fails mid-task?
- Workflows are designed to be restartable. We use a Supabase database to track the state of each task. If an agent fails to parse a file, the Coordinator agent logs the error, sends a Slack alert with the file name, and moves on to the next task. It will re-attempt the failed task three times before marking it for manual review.
- How is this better than using a platform like Microsoft Copilot Studio?
- Copilot Studio is great for building chatbots or agents inside the Microsoft ecosystem. Syntora builds agents that connect to any tool with an API, including your custom internal software. We provide the full Python source code, so you have total control and are not locked into a specific vendor's platform or pricing model.
- Can these agents handle unpredictable human emails?
- Yes, within limits. We use Claude 3's reasoning to interpret intent from unstructured text. For a client in shipping, an agent reads inbound emails to extract company name, container ID, and desired delivery date. It successfully processes about 85% of emails automatically. The remaining 15% with ambiguous or missing info are flagged for human review.
- Who handles the API keys and security?
- You do. We deploy the system into your own AWS account. All API keys and credentials are stored securely in AWS Secrets Manager, which you control. Syntora does not store any of your sensitive credentials. This gives you full ownership and oversight of the system's security posture from day one.
- What is the typical maintenance cost after handoff?
- For most systems, the only ongoing costs are for AWS Lambda and the Claude API, typically under $100 per month combined for moderate usage. We build the system to be self-sufficient. If a third-party API you use has a breaking change, that would require a small code update, which we can handle on an hourly basis or through an optional support plan.
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