Governed agent systems
Give software the ability to act—with boundaries.
We build semi-autonomous agents that use tools, retrieve knowledge, coordinate tasks, and ask for approval when judgment or authority belongs with a person.
01 / Business challenge
The problem behind the technology.
Agent prototypes often hide state, permissions, cost, and failure recovery behind an impressive chat. Reliable systems need explicit tools, durable context, audit trails, budgets, and ways for people to interrupt or approve work.
02 / What we build
Capabilities assembled around the outcome.
The exact mix follows the product, data, risk, and operating environment—not a fixed technology package.
Customer-support and service assistants
Document-processing and operations agents
Developer and technical workflow agents
Multi-agent orchestration and task routing
MCP integrations, memory, approvals, and audit trails
03 / Typical use cases
Where this capability creates leverage.
- Research agent that gathers and organizes sources
- Support assistant that drafts answers and escalates exceptions
- Operations agent that updates approved systems
- Document agent that extracts, checks, and routes cases
- Developer agent operating inside constrained repositories
04 / Delivery approach
Reduce uncertainty in the right order.
- 01
Choose a bounded job with a clear owner
- 02
Define tools, permissions, data, and prohibited actions
- 03
Design memory and state around the actual workflow
- 04
Add human approval and deterministic checks at risk points
- 05
Evaluate task success, cost, failure recovery, and traceability
05 / Technology context
Standard tools, selected for the system.
Technology choices are validated against team capability, security, cost, integration, and long-term ownership.
- Python
- TypeScript
- LLM APIs
- MCP
- Tool calling
- Vector search
- Queues
- PostgreSQL
- FastAPI
- OpenTelemetry
06 / Relevant engineering references
Systems worth studying.
Independent open-source and research projects, clearly attributed and examined for transferable engineering lessons—not presented as NexLoomix client work.
07 / Connected capabilities
Most products cross disciplines.
08 / Questions
Before an engagement begins.
Are the agents fully autonomous?
Usually not. We favor bounded autonomy: software can perform defined work while sensitive, irreversible, ambiguous, or high-impact actions require approval or escalation.
What is MCP used for?
Model Context Protocol can provide a consistent boundary between an agent and approved tools or data sources. It does not replace authentication, authorization, validation, or audit controls.
Can agents work with our existing systems?
Yes, when those systems expose suitable APIs or controlled interfaces. Discovery maps permissions, rate limits, failure handling, and ownership before integration.
AI Agents
Bring the business problem.
We will help define the right first decision, the necessary evidence, and a credible path to a working product.