Agentic Workflows
Transition from brittle, linear automation scripts to adaptive, resilient multi-agent collaboration ecosystems.
Defining Agentic Workflows
Agentic workflows refer to dynamic execution graphs where interconnected AI agents use language models to reason about a goal, formulate a plan, and execute API calls, maintaining shared temporal state until the task is complete.
In standard automation (e.g., Zapier, Make, n8n), business logic is defined by predetermined paths. If the data format changes slightly, the workflow breaks. In an agentic workflow managed by Stellaris AOS, the agent understands the intent and can self-correct or ask a human for clarification if it encounters an anomaly.
Multi-Agent Coordination Architecture
Enterprise tasks are too complex for a single "god prompt" LLM. Stellaris AOS utilizes a multi-agent topology:
Intent Router (Supervisor)
Analyzes incoming triggers (emails, support tickets) and routes them to the appropriate specialized sub-agent.
Specialist Agents (Workers)
Agents with narrow scopes (e.g., SQL Data Extractor, CRM Updater) that focus purely on their domain expertise constraint.
State Synthesizer
Consolidates the outputs from multiple agents into a unified, formatted response payload or API mutation.
RPA vs. Agentic Routing
Scenario: Processing an incoming vendor invoice that contains a varying internal layout.
TRADITIONAL RPA PIPELINE
2. Push to OCR template parser.
3. If coordinates mismatch -> Throw Error -> Manual Review.
-> Fails often due to unstructured variability.
AGENTIC ORCHESTRATION PIPELINE
2. Agent uses Vision LLM to semantically understand the document, regardless of layout.
3. Extracts JSON payload based on strict schema.
4. Supervisor Agent verifies extracted data against existing ERP records via Retrieval (RAG).
5. Action Agent drafts ERP insert -> Pauses for HITL Approval.
-> Resilient, self-correcting, completely deterministic payload.