AI systems beyond the prototype.
Sabentech designs and operates software systems that combine AI models, agents, tools, data and infrastructure. Our engineering work focuses on the parts that turn an AI capability into a usable system: orchestration, integration, reliability, security, observability and deployment.
Agents that can reason, use tools and operate within defined boundaries.
We build systems where AI agents can interact with APIs, data sources, software tools and other agents to complete multi-step tasks.
Agent orchestration
Coordinating agents, models, tools and workflows.
Tool execution
Connecting agents to APIs, services and controlled external actions.
Model routing
Selecting models based on task requirements, cost and capability.
Human approval
Adding approval steps before sensitive or irreversible actions.
Memory & context
Managing persistent information and task-specific context.
Multi-agent workflows
Delegating work across specialized agents while maintaining control over execution.
Models are only one part of the system.
Production AI requires more than sending a prompt to an API. We work on the surrounding engineering required to make LLM-powered applications usable in real environments.
AI systems fail differently from traditional software.
Models can return unexpected outputs. External APIs can become unavailable. Long-running agents can partially complete tasks before failing. The surrounding system needs to anticipate these conditions.
Retries & timeouts
Handling temporary failures without creating uncontrolled execution loops.
Idempotency
Preventing duplicated actions when tasks are retried.
State management
Tracking where a long-running workflow is and what has already happened.
Failure isolation
Containing failures so that one task does not compromise the rest of the system.
Execution limits
Controlling runtime, cost and resource consumption.
Autonomous systems need explicit boundaries.
Agentic systems can interact with external services and perform actions. That makes permission design and isolation part of the architecture.
The goal is not unrestricted autonomy. The goal is useful automation with clearly defined authority.
Connect models to the information they actually need.
We build retrieval-based systems that allow AI applications and agents to work with private documentation and structured information.
AI has to run somewhere.
We work across the infrastructure needed to deploy and operate AI-powered software.
The deployment model depends on the application, data requirements and operational constraints.
Moseisley
Agent orchestration and autonomous workflows.
Moseisley is an AI agent orchestration system designed around a continuous execution loop:
Engineering work around the system
Agent orchestration
Delegation of tasks across specialized agents and tools.
Controlled tool execution
Agents interact with external capabilities through defined interfaces rather than unrestricted system access.
Human approval
Sensitive actions can require explicit approval before execution.
Cost controls
Model usage can be constrained using budgets, quotas and provider-level controls.
Multi-tenant boundaries
Agent execution and data access are scoped between users and environments.
Mission tracking
Agent actions and execution states can be recorded and surfaced through mission timelines and activity events.
Isolated external research
Internet-facing tasks can be delegated to disposable isolated agents designed to reduce exposure between untrusted external content and trusted internal systems.
Other systems built by Sabentech.
MagicConnect
Conversational AI and digital human experiences. Real-time AI interaction combining conversational models, voice and avatar interfaces.
AlloLaura
AI voice automation. Voice-based AI systems designed to handle business conversations and workflow automation.
The exact stack depends on the problem.
Current areas of work.
AI-assisted development is part of our engineering workflow.
AI can accelerate implementation, debugging and exploration, but architecture, integration, testing, deployment and technical decisions still require human responsibility.
The objective is not to write code manually for its own sake. The objective is to build systems that can be understood, tested, operated and improved.
A consistent method, regardless of scope.
Understand the system
Define the problem, interfaces, data, constraints and failure modes.
Design the architecture
Decide how models, services, databases and tools interact.
Build incrementally
Implement small components that can be tested independently.
Test failure cases
Evaluate what happens when models, APIs, workers or external systems fail.
Deploy and observe
Run the system in its real environment and collect operational feedback.
Improve
Use real behavior to refine architecture, prompts, models and workflows.
Looking for AI engineering expertise?
Whether you are building an AI product, integrating agents into an existing system or trying to move an AI prototype toward production, we can discuss the technical problem first.