Build Production-Grade Runtime Control for Your AI Agents
AI agents need managed infrastructure for reliable runtime control, not a pure DIY approach. DIY control logic often fails under concurrent loads and lacks proper state management.
Syntora designs custom orchestration layers for AI agent runtime control, providing reliable state persistence, task queuing, and human escalation points. Leveraging expertise from building similar document processing pipelines for financial clients, Syntora approaches agent systems with a focus on detailed workflow mapping and robust serverless architectures. This ensures complex multi-agent systems operate efficiently and can be managed effectively.
Runtime control includes state persistence, task queuing, error handling, and human escalation points. A simple agent that summarizes articles requires minimal infrastructure. A system that processes customer orders, interacts with multiple APIs, and requires approval steps needs a dedicated orchestration layer to be reliable.
Syntora designs and builds custom orchestration layers for complex agent workflows. The scope of such a system depends on the number of agents, the complexity of inter-agent communication, external API integrations, and the required human intervention points. We have experience building similar document processing and workflow automation pipelines using Claude API for financial services clients, and the same architectural patterns apply to managing AI agent runtimes. A typical build for this kind of system ranges from 8 to 16 weeks, requiring active collaboration from your team to define workflows and data sources and integrate with existing systems.
What Problem Does This Solve?
Most teams start by writing a single Python script. This works for one-off tasks but fails as a runtime system. When ten webhook events fire at once, the script either processes them sequentially, creating massive delays, or it crashes from memory overload. State is stored in memory, so any crash means the agent's progress is lost completely.
A natural next step is to try a data workflow orchestrator like Airflow. These tools are built for batch data processing, not for reactive, event-driven agents. Their directed acyclic graph (DAG) model cannot handle the dynamic, looping, and long-running nature of agentic tasks. An agent that must wait 24 hours for human input before deciding its next step breaks the Airflow paradigm, which expects tasks to complete quickly.
This leads teams to generic agent platforms. These platforms provide a UI but hide the control layer. You cannot implement custom exponential backoff for a flaky API, store state in your own production database, or trigger a specific sub-agent based on complex business logic. When a workflow fails inside this black box, you get a generic 'Error' message with no logs, no context, and no way to debug or resume.
How Would Syntora Approach This?
Syntora's approach to AI agent runtime control begins with a detailed discovery phase to map your entire workflow into a state machine, often using tools like LangGraph. This process would define every possible state, such as 'Awaiting Human Input' or 'Enriching Data From API', and the valid transitions between them. For state persistence, we would typically implement a Supabase Postgres database, creating a dedicated table to track each agent's execution history, current state, and payload. This design ensures that if a process is interrupted, it can reliably resume from its last known good state.
The core of the system would be a supervisor agent, implemented as a Python application using FastAPI. This supervisor would read the current state from Supabase, determine the next action based on the defined state machine, and then invoke specialized sub-agents to perform specific tasks. Each sub-agent would be deployed as an isolated AWS Lambda function. This serverless architecture would provide automatic scalability to handle concurrent executions without manual provisioning.
Workflows would typically be initiated by webhooks hitting an AWS API Gateway endpoint. For tasks requiring delays, such as sending a follow-up email after a set period, the supervisor would avoid long-running processes. Instead, it would write a 'wakeup' timestamp to the state table, and an AWS CloudWatch rule would trigger the agent again at the precise time. This event-driven pattern is highly efficient for managing numerous asynchronous tasks.
When an agent encounters a situation it cannot handle, the system would be designed for escalation. The supervisor would update the state to 'Requires Human Review' and use an integration like the Slack API to send a notification, potentially including action buttons like 'Approve', 'Retry', or 'Abort'. For debugging and auditability, all agent actions, LLM prompts, and API responses would be logged as structured JSON using a tool like structlog. This provides clear visibility into agent behavior and assists in rapid issue diagnosis.
What Are the Key Benefits?
Your System Deploys in 3 Weeks
We go from workflow diagram to a production-ready system in 15 business days. No long R&D cycles or internal teams learning new frameworks.
Pay for Execution, Not Idle Time
Our serverless architecture on AWS Lambda means you pay per-millisecond of use. A workflow that runs 1,000 times a month costs less than $50, not a fixed server fee.
You Get the Keys and the Blueprints
You receive the full Python source code in your own GitHub repository, plus a runbook detailing the architecture and maintenance procedures.
Failures Alert You with Context
Instead of silent fails, the system send specific Slack alerts when a task fails after 3 retries, including the exact input that caused the error.
Connects to Your Tools via API
The system integrates directly with any tool that has a REST API, like HubSpot, Zendesk, or a custom internal database. No brittle UI-based scraping.
What Does the Process Look Like?
Workflow Mapping (Week 1)
You provide access to relevant APIs and walk us through the workflow. We deliver a detailed state machine diagram and an architectural plan for your approval.
Core Agent Development (Week 2)
We build the supervisor and sub-agent functions in Python. You receive access to a staging environment to test the agent's logic with sample data.
Integration and Deployment (Week 3)
We connect the system to your production services via webhooks and deploy it to your AWS account. You receive the complete source code and infrastructure-as-code files.
Monitoring and Handoff (Weeks 4-6)
We monitor the live system for two weeks, tuning performance and handling edge cases. You receive a final runbook and we transition to an optional monthly support plan.
Frequently Asked Questions
- How much does a custom agent system cost to build?
- The scope depends on the number of integrations and the complexity of the agent's decision logic. A single-purpose agent connecting two APIs is a straightforward 3-week build. A multi-agent system coordinating five or more services with human-in-the-loop escalation requires more discovery. We provide a fixed-price proposal after our initial discovery call.
- What happens if a third-party API like Claude is down?
- The agent's state is persisted in Supabase. If an API call to Claude fails, our code implements exponential backoff, retrying three times over 90 seconds. If it still fails, the state is set to 'paused_api_error' and a Slack alert is sent. The workflow can be resumed from the exact point of failure once the API is back online, without losing data.
- How is this better than using a platform like LangChain or LlamaIndex?
- LangChain and LlamaIndex are excellent libraries for agent components, and we use parts of them. However, they are not a complete runtime infrastructure. They don't provide state persistence, serverless deployment, task orchestration, or human escalation out of the box. We build that critical infrastructure around the agent logic, which is what makes a system production-ready and reliable.
- Can I manage the system myself after you build it?
- Yes. The system is deployed in your own AWS account using Terraform. The provided runbook documents how to deploy changes, view logs, and handle common alerts. Any Python developer can maintain and extend the system. We are not a black-box platform; you have full control and ownership of the code and infrastructure.
- How do you handle secrets and API keys securely?
- We never hardcode secrets. All API keys and database credentials are stored in AWS Secrets Manager. The AWS Lambda functions are granted specific IAM roles that give them permission to retrieve only the secrets they need at runtime. You have full control over these secrets and can rotate them at any time without requiring code changes.
- What kind of performance can I expect?
- For most webhook-driven workflows, the end-to-end processing time from trigger to completion is under two seconds, excluding any intentional waits or third-party API latency. The serverless architecture scales automatically, so performance remains consistent whether you have 10 or 10,000 executions per day. We establish specific performance benchmarks during discovery.
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