Multi-Agentic Business Process Automation using Agentic AI - LangGraph

Organizations use rule-based automation tools, RPA bots and ad‑hoc Python scripts to automate Business Processes. While these solutions deliver short‑term gains, they quickly become brittle, costly to maintain, and ill‑equipped to handle exceptions or evolving business rules.

Agentic AI tools like LangGraph which is opensource multi‑agent orchestration framework—replaces the limitations of RPA and script‑based automation with a resilient, flexible, intelligent AI‑driven architecture that unifies stateful workflows, human‑in‑the‑loop controls, and adaptive learning.

LangGraph native multi‑agent orchestration empowers AI agents to dynamically negotiate, delegate, and execute parallel tasks, automatically rebalancing workflows when exceptions occur. Its stateful conversational memory records every interaction and data point in a shared context, ensuring full traceability and auditability of decisions, handoffs, and human interventions. Built‑in human‑in‑the‑loop controls and interrupt mechanisms let agents flag anomalies and enable supervisors to seamlessly pause, reroute, or enrich data within the same process.

We used LangGraph to automate Claims Management process with Human-in-the-Loop feedback mechanisms from the initial First Notice of Loss (FNOL) to final settlement and recovery. The integration of LangGraph’ s interrupts, commands, and feedback loops ensured that human intervention is possible whenever necessary, without derailing the automated flow.

Below are the steps for Claims Management that demonstrate the use of Agentic AI to automate the complete process:

Step 1: Initiating a Claim - The First Notice of Loss (FNOL)

The journey begins when a policyholder experiences an incident and needs to file a claim. We used GenAI-enabled chatbot, acting as the Claims Intake Agent to initiate the FNOL process.

• The chatbot collects essential claim details interactively.
• It guides the user to upload supporting documents such as: Police reports Repair estimates Auto accident images Other required evidence

Real-time validation checks powered by AI ensure that all required information is complete before submission.

Once the FNOL is captured, the data and documents are stored in the Claims Database, ready for downstream processing.

Below is the schematic representation of solution flow using LangGraph:

Step 2: Orchestrating Claims Processing with Specialized Agents

Once Claim is submitted, Claims Manager Agent takes charge of orchestrating the next stages of the claim’s lifecycle:

2.1 Claim Notification and Registration

The Claims Manager Agent delegates the claim to a Claims Lodging Agent. This agent is responsible for:

• Verifying the completeness of the FNOL package
• Notifying internal stakeholders
• Officially registering the claim into the policy administration system

Leveraging LangGraph task delegation and state transition capabilities, the Lodging Agent ensures a clean handoff to the next phase once registration is successful.

2.2 Claim Investigation and Valuation

Upon successful registration, the Manager Agent assigns the claim to the Claims Handling Agent, who performs:

• Claim investigation
• Damage assessment and valuation
• Fraud checks if necessary

This agent interacts asynchronously with external systems (e.g., accident databases, valuation engines) using LangGraph tool integrations and external API calls.

Throughout this phase, human-in-the-loop is seamlessly integrated:

• If anomalies are detected (e.g., incomplete information, suspected fraud), an interrupt is triggered.
• A human claims adjuster is notified and can command the agents to pause, reassign, or gather more information.

Step 3: Claim Decision, Settlement, and Recovery

After valuation, the Claims Decision Agent steps in. It is responsible for:

• Recommending claim settlement decisions
• Processing payouts
• Managing subrogation and recovery if applicable

Using a combination of expert valuation, business rules, and GenAI reasoning, the Decision Agent finalizes the claim, subject to human approval for high-value or complex cases.

Again, LangGraph command and interrupt features empower supervisors to step in, modify settlement recommendations, or trigger escalation workflows — ensuring alignment with business policies and risk guidelines.

In conclusion, LangGraph multi-agentic AI, combined with human-in-the-loop feedback mechanisms, heralds a new era for Business Process Automation where intelligent automation, collaboration, and adaptability coexist harmoniously. It elevates decision-making, empowers human agents, and transforms traditional workflows into intelligent, living ecosystems.

About the Author

Dilbagh Dhindsa, Practice Head – AI and GenAI, at Aress is a hands-on leader in Generative AI, AI/ML engineering, Data Science, and software development with over 20 years of International experience. He has developed groundbreaking AI and Generative AI solutions for global customers that helped solve complex business problems and optimize processes.

He developed GenAI Accelerators for generating Sections of SoW(Statement of Work) using innovative metadata-driven dynamic chunk mapping. A US patent has been filed for the solution. Other GenAI Solutions included Secure Private GPT, an Email processor for license information, a Recruitment tool for matching JD with resumes and chat.

Under Dilbagh's leadership, our Gen AI practice is flourishing. Contact Dilbagh today to discover how his expertise can take your business to the next level.