Agentic artificial intelligence system
An agentic artificial intelligence system processes insurance claims, medical claims, financial transactions, and sales leads by receiving and preprocessing claimant, patient, transaction, and prospect data to standardize formats, remove sensitive identifiers, and enrich records. It uses machine learning, deep learning, natural language processing, and computer vision to analyze both structured and unstructured data, identify errors, inconsistencies, or fraudulent patterns, verify eligibility and compliance, and assign relevant codes based on historical and contextual information. The system calculates expected payouts or reimbursements, assesses transaction feasibility, and generates risk scores while adapting its predictions to market conditions, contractual factors, or clinical guidelines. A multi-agent framework coordinates specialized agents for eligibility verification, coding, pricing, fraud detection, and sales outreach, supporting multi-channel communication, lead prioritization, and natural language generation of outreach messages and decision-making explanations. Continuous learning is achieved via retraining, feedback loops, federated learning, and blockchain-based recordkeeping, ensuring secure, transparent, and compliant operations across multiple domains.
1 . A computer-implemented method for processing a data object using an agentic artificial intelligence (AI) system, the method comprising:
providing a plurality of specialized AI agents in a parallel processing pipeline to perform the following:
preprocessing the data object by enriching the data object with information from internal or external sources, and removing or pseudonymizing personally identifiable information;
analyzing, by at least one specialized AI agent, the preprocessed data object based on predefined or learned criteria;
verifying, by at least one specialized AI agent, the data object with reference profiles, historical data, or external databases;
assigning, by at least one specialized AI agent, one or more classifications, codes, or scores to the data object using machine learning algorithms trained on historical or domain-specific data;
calculating, by at least one specialized AI agent, an expected outcome, recommendation, or action for the data object based on the assigned classifications, codes, or scores;
predicting, by at least one specialized AI agent, a likelihood metric, for data objects exceeding a threshold for the likelihood metric, initiating one or more verification steps or follow-up actions;
initiating, by the agentic AI system, one or more automated workflows or communications based on the analysis and predicted outcomes, including updating internal systems, generating personalized messages, or routing the data object to a reviewer; and
continuously learning, by the agentic AI system, from the processed data object, user feedback, or outcome data.
2 . The method of claim 1 , comprising:
receiving a claim including claimant data, incident information, and coverage details;
analyzing, by a first AI agent, claim data to identify potential errors, inconsistencies, or missing information;
verifying, by a second AI agent, claim eligibility by querying external databases and cross-referencing with policy information;
assigning, by a third AI agent, one or more claim codes using machine learning algorithms trained on historical coding data;
calculating, by a fourth AI agent, the expected payout amount based on the assigned codes, policy terms, and historical claim data;
predicting a likelihood of claim fraud by a fifth AI agent trained on historical claims data and for claims with high fraud probability, initiating additional verification steps and gathering supporting documentation.
3 . The method of claim 1 , comprising:
generating a context object comprising data about a current webpage, a browsing history, and user preferences;
transmitting a user query and the context object to the agentic AI agent;
receiving a response from the agentic AI agent, wherein the response is selected from the group consisting of a textual response, a webpage element suggestion, and a navigation instruction and
when the response type is a textual response, displaying a text overlay on the current webpage with the textual response;
when the response type is a webpage element suggestion, visually highlighting a specified element on the current webpage; and
when the response type is a navigation instruction, automatically navigating to a uniform resource locator (URL) specified in the navigation instruction.
4 . The method of claim 1 , comprising routing data to the plurality of specialized AI agents, including: a data validation agent configured to identify missing, inconsistent, or erroneous information; a vision agent configured to analyze images or video for assessment using neural networks; a rule-based expert system agent; a verification agent for verifying claim eligibility by querying internal and external data sources.
5 . The method of claim 3 , comprising processing data with a browser or an application embedded with an AI neural network.
6 . The method of claim 1 , comprising:
generating a risk score for a claim based on claimant history, claim amount, and incident detail;
routing first risk claims for fraud investigation; and
expediting second risk claims for processing.
7 . The method of claim 1 , wherein specialized AI agents handle different aspects of the claim process, comprising coordinating the actions of the agents using a master orchestrator agent and aggregating the outputs from each agent to make a final claim decision.
8 . The method of claim 1 , comprising:
utilizing a machine learning model trained on historical claim data to predict a settlement amount;
adjusting the settlement amount based on current market conditions and regional factors;
comparing the settlement amount with similar past claims for consistency.
9 . The method of claim 1 , comprising:
assigning claim codes via a reinforcement learning model that optimizes code selection through iterative reward signals derived from prior claim adjudication outcomes;
calculating an expected payout amount using a federated learning framework that aggregates historical claim data across partitioned datasets while maintaining data locality compliance;
predicting fraud likelihood via an adversarial neural network trained to detect synthetic claim patterns, wherein the network ingests real-time IoT data streams and applies temporal convolution to identify anomalous sequences;
updating claim status via a state machine that triggers context-aware follow-up actions using robotic process automation (RPA) bots interfaced with legacy insurance systems.
10 . The method of claim 1 , comprising:
assigning, by a master orchestrator agent, the plurality of specialized artificial intelligence (AI) agents to process respective components of the claim data in parallel;
coordinating, by the master orchestrator agent, inter-agent communication, including routing intermediate outputs between agents using data channels and resolving conflicts between agent conclusions using attention-based preference weighting; and
aggregating, by the master orchestrator agent, outputs from the specialized AI agents by:
computing confidence-weighted embeddings of each agent's finding;
applying domain-specific alignment layers to normalize semantic representations;
generating a final claim decision through fused multi-modal reasoning.
11 . The method of claim 3 , comprising:
coordinating, by a master orchestrator agent, inter-agent communication, including routing intermediate outputs between agents and resolving conflicts between agent conclusion; and
aggregating, by the master orchestrator agent, outputs from the specialized AI agents.