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Risk Orbit: UiPath Coded Agents Meet Real-World Supply Chain Risk

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AI Agent Architect at Ashling | 2x UiPath MVP Award | UiPath Dallas Chapter Lead
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Recently, UiPath named Ashling's Risk Orbit a Top 7 winner in the global UiPath Specialist Coded Agent Challenge. The project was led by our Solution Architecture Sr. Consultant, Naveen Chatlapalli, a two-time UiPath MVP award winner.
 
Risk Orbit shows how quickly we can now move from idea → working coded agent → enterprise-ready pattern, and what that speed unlocks for our clients at Ashling.

 

The Supply Chain Risk

Modern supply chains live in a constant state of change. Trade routes can shift overnight. New tariffs and regulations appear with little warning. Geopolitical events change risk profiles in an instant, often before decision-makers even realize what has happened.
 
Operations leaders know the risks are out there, but rarely see them in one place. Instead, they piece together exposure from spreadsheets, manual checks, and scattered reports across teams and systems. That way of working is slow, reactive, and nearly impossible to scale as complexity grows.
 
“What if a team of AI agents could constantly scan the risk landscape and surface the right insight, to the right person, at the right time—without adding more dashboards?”

That question became Risk Orbit.

 

Introducing: Risk Orbit

Risk Orbit is a multi-agent geopolitical and trade risk intelligence system that continuously evaluates shipments, routes, suppliers, tariffs, and external events, then turns those signals into clear guidance for operations teams.
 
The system evaluates risk behind the scenes. Stakeholders receive concise, contextual recommendations they can act on immediately.
 
Under the hood, Risk Orbit combines UiPath Coded Agents and UiPath Maestro for orchestration, LangChain-style agentic AI for deeper reasoning over documents, MCP (Model Context Protocol) servers as governed knowledge layers, and external APIs for live operational context. The result is not a single bot, but a coordinating system of agents that behave as one.
 
Book a Risk Orbit demo

 

How Risk Orbit Works

1. Maestro Orchestrates the Workflow

The process begins in UiPath Maestro. Maestro monitors incoming emails and identifies shipments that require risk analysis. When it detects a relevant case, it triggers the Risk Orbit workflow, passing along all of the details that downstream agents will need. 

The orchestration begins as soon as the business signal appears.

 

2. APIs Provide Operational Context

Before any AI reasoning happens, Risk Orbit gathers the operational context that will ground every decision. Maestro pulls contextual data from Equipment APIs, Supplier, and Scheduler APIs, giving the agents a full picture of what, where and when. 

Maestro calls a set of APIs, such as equipment, equipment supplier, and scheduler services, to answer practical questions: what is moving, who is responsible, where it needs to go, when it has to arrive.

 

3. Geo-Political Coded Agent
The first intelligence layer is the Geo-Political Coded Agent. This agent calls a port status service to check for closures, strikes, congestion, or capacity issues along the route. It also queries a travel advisory service to understand region-specific safety alerts and unrest notices.
 
The most important source sits behind an MCP Server. The Geo-Political MCP Server holds curated, governed knowledge about conflict zones, sanctioned regions, political instability indicators, and escalation signals. Rather than scraping the open internet and hoping for the best, the agent calls the MCP endpoint to fetch trusted, governed geopolitical intelligence.
 
It then synthesizes these inputs into a risk score and preliminary recommendation.
 

4. LangChain Coded Agent – Tariff & Trade Policy Analysis
Risk Orbit then layers in a second perspective through a LangChain-powered agent focused on tariffs and trade policy and with the ability to handle complex reasoning over unstructured documents. This agent uses:
 
  1. Contextual Grounding Tool: Downloads and indexes Tariff Policy and Trade Policy documents. It provides grounded, citation-backed answers instead of hallucinations.
  2. LLM Reasoning & Variability Tool: Uses LangChain’s tool routing, dependency chains, and agent planner patterns to extract country-specific duty rates, rule changes, exemptions, and compliance requirements.
  3. Geo-Political MCP Cross Check: The LangChain agent also calls the MCP server as a tool to cross-validate geopolitical context against tariff implications—something traditional RPA bots cannot do. This fusion of UiPath + LangChain + MCP is what turns Risk Orbit into a true agentic intelligence system.

5. Synthesis & Recommendations
Once both intelligence layers have done their work, Agent Builder brings everything together.
 
Agent Builder ingests the geopolitical risk signals, the tariff and policy interpretation, the MCP knowledge, and the operational data from internal APIs. It then produces a single combined risk score for the shipment and a clear recommendation on what to do next.
 

That recommendation falls into a simple set of actions: proceed as planned, monitor and review, reroute to a safer or more efficient option, or escalate for human approval.

UiPath handles the final mile. Maestro sends a concise email summary to the right stakeholders or triggers downstream workflows and approvals in the systems they already use. The business sees a focused decision that is easy to understand and easy to act on.

 

Why This Architecture Works

  • Enterprise Governance & Rapid AI Experimentation
    UiPath provides governance; LangChain provides the reasoning engine. MCP provides trusted domain knowledge. Together, they create a stack that lets us experiment quickly while staying within the guardrails that risk, security, and compliance teams require.
  • Multi-Agent Patterns That Scale

    Each agent plays a clear, focused role. The Geo-Political Agent watches external events and ports. The LangChain Trade Agent interprets policy. MCP servers provide authoritative knowledge that both agents can rely on. Maestro coordinates the flow of work and plugs the outputs back into business processes. Because responsibilities are well defined, we can adapt this pattern to new domains without rewriting everything from scratch. New agents can join the system. New MCP servers can introduce fresh knowledge. The orchestration model remains the same.

  • Rapid Iteration With Real-World Feasibility
    This architecture also changes how we prototype. We no longer have to choose between fast proof of concept work and enterprise-grade reliability. Risk Orbit shows that we can design for both from the beginning.
 
 
How We Stay Ahead As Capabilities Evolve

  1. Use Hackathons as R&D Labs: We treat hackathons and competitions as an R&D lab for agentic patterns. The strongest concepts, like Risk Orbit, graduate into reusable patterns that can serve multiple clients.

  2. Learn through practice, not theory: at Ashling, we make a habit of building small proof of concept projects whenever new capabilities appear. That helps shorten the gap between emerging capability and client-ready solutions.
  3. Treat MCP Servers as knowledge abstractions: By treating these servers as modular knowledge abstractions, we can swap in new content without redesigning the entire architecture.
 

What Comes Next

Risk Orbit is a blueprint for how agentic automation can reshape risk and supply chain operations.

The same architecture can extend to ESG and sustainability risk, trade compliance and screening, supplier performance and scorecards, financial risk and exposure workflows, and logistics planning and optimization. Everywhere there is complex, fast moving information and a need for timely, explainable decisions, the pattern applies.

As agentic AI matures, we are focused on keeping Ashling and our clients ahead of the curve, combining UiPath, LangChain, and MCP to solve real-world challenges with speed, precision, and control.

If your risk and supply chain teams still rely on spreadsheets and scattered dashboards, Risk Orbit is a preview of what a coordinated team of AI agents can do next.​

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