Estimated Read Time: ~10-12 minutes
Target Audience:
- UiPath CoE Leaders
- Solution Architects
- Automation Developers
- AI/ML Engineers
- Business Process Owners exploring Agentic Automation
UiPath has been rapidly expanding its Agentic Automation capabilities — moving beyond classic RPA into autonomous agents that combine:
AI Reasoning (LLMs)
RPA & API Execution
Business Context & Memory
Orchestration & Governance
Secure Enterprise-Grade Automation
At the center of this evolution: UiPath Coded Agents.
This article is your complete practical guide — explaining what Coded Agents are, when to use them, how to build them, and why they matter.
What Are UiPath Coded Agents?
A Coded Agent is:
- A fully programmable, AI-powered agent written in Python or Node.js
- Hosted, governed, monitored inside the UiPath Automation Cloud
- Directly integrated into UiPath’s platform (context, governance, security, toolsets)
Think of Coded Agents as:
“Bring your own advanced AI logic — while letting UiPath handle orchestration, security, deployment, and governance.”
Coded Agents vs Traditional RPA / Agent Builder
| Capability | Traditional RPA | Agent Builder | Coded Agents |
|---|---|---|---|
| Developer Skill | Low-code | Low-code | Full-code |
| AI Reasoning | Minimal | Basic LLM Reasoning | Full AI Planning / LLM Orchestration |
| Customization | Workflow Driven | Declarative Agent Setup | Full Programming Control |
| Governance | |
|
|
| Use Cases | Standard Automation | Simple AI Agents | Advanced AI Agents |
Where Coded Agents Fit Inside UiPath Agentic Automation Stack
Coded Agents are one pillar of UiPath’s agent ecosystem:
- Low-Code Agents (drag-drop studio agents)
- Agent Builder (low-code agentic LLM orchestration)
- Coded Agents (fully custom reasoning + orchestration logic)
Coded Agents give maximum control when you need:
- Complex reasoning loops
- Custom memory structures
- Multiple LLM integrations
- Advanced planning & decision trees
- Dynamic tool discovery (MCP integration)
How Coded Agents Work (Under the Hood)
Write agent logic using the UiPath SDK (Python 3.9+ or Node.js)
Integrate any LLM (OpenAI, Azure OpenAI, Anthropic, UiPath AI Fabric)
Register tools dynamically via SDK
Leverage memory (short-term, long-term, external memory providers)
Deploy into UiPath Automation Cloud as a governed agent
Observe, monitor, and control execution through UiPath governance layers
What’s Included in the SDK?
- Agent lifecycle orchestration
- LLM orchestration utilities
- Memory APIs (short-term + long-term state)
- Tool registration APIs
- Secure enterprise context injection
- Exception handling & observability
Practical Example: Refund Processing Agent (MCP Integration)
Business Problem:
Customer sends refund requests via email → agent autonomously validates → triggers refund → sends confirmation.
Coded Agent Flow:
Ingest request (email parsing via DU or mailbox integration)
Classify intent via LLM (“Refund” vs “Inquiry” vs “New Order”)
Validate refund eligibility via internal APIs
Dynamically discover refund APIs via MCP Servers
Trigger refund via Stripe MCP Server
Send confirmation email to customer
Python SDK Code Sample:
python
CopyEdit
from uipath_agents_sdk import Agent, Tool
class RefundAgent(Agent):
def __init__(self):
super().__init__()
self.register_tool(Tool(
name="refundTransaction",
description="Refund customer via Stripe",
input_schema={"transaction_id": "string", "reason": "string"},
function=self.refund_transaction
))
def refund_transaction(self, transaction_id, reason):
stripe_mcp_client = StripeMCP(server_url, api_key)
return stripe_mcp_client.refund(transaction_id, reason)
def on_message(self, message):
intent = self.llm.classify(message, labels=["refund", "payment", "other"])
if intent == "refund":
transaction_id = extract_transaction_id(message)
reason = extract_reason(message)
return self.invoke_tool("refundTransaction", transaction_id, reason)
return "No action required."
Why Enterprise CoEs Should Care
| Benefit |
 Outcome |
|---|---|
| Governance | Full security, credential management, RBAC via UiPath |
| Scalability | Build once — reuse everywhere inside platform |
| Observability | Native logging, monitoring & troubleshooting |
| Tool Expansion | Leverage MCP + Tooling dynamically |
| Hybrid AI | Use multiple AI models for different reasoning tasks |
Troubleshooting Resources
Official UiPath troubleshooting guide:
Common Issues To Watch:
- Tool registration errors
- API key management via UiPath Key Vault
- LLM response parsing
- Schema mismatches
- Memory overflow in long-running tasks
Practical Getting Started Steps
Install Python or Node.js SDK
Build simple agent locally
Test basic LLM interaction + tool registration
Deploy into Automation Cloud (Agents workspace)
Test end-to-end flows (inputs, tool calls, outputs)
Set up monitoring + error handling
Start scaling with MCP Servers and memory management
The Bigger Picture — Future of Coded Agents
UiPath Coded Agents will power:
- Autonomous automation flows
- Self-healing agentic workflows
- Multi-agent orchestration (in combination with Maestro BPMN)
- Real-time LLM decisioning
- AI-powered process augmentation (human-in-the-loop + autonomous)
Key Takeaway
Coded Agents = Full control over AI reasoning, with UiPath governance.
Perfect for advanced, complex, business-critical AI agents that require safety, scale, and flexibility.
Let’s hear from you!
Have you started exploring UiPath Coded Agents? What use cases are you building?
Drop your thoughts, questions, or challenges in the comments below.
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