Flink Agents - Use Cases and Gap Analysis

[thefalc]

Overview

This document outlines a set of representative enterprise use cases where a multi-agent system offers a practical, scalable solution. Each use case includes:

• A description of the real-world problem
• A proposed architecture using multiple specialized agents
• An analysis of technical requirements for the use case
• A gap analysis on what is missing from Apache Flink today to implement the solution cleanly

The goal is to ground the development of the Flink Agents in concrete, high-value scenarios, demonstrating the need for native agentic workflows within Flink and guiding the feature roadmap based on real-world demands.

We don’t need to address all gaps immediately. We should focus on the minimal set of gaps to address a sub-set of use cases for MVP and define which requirements are needed for MLP.

Table of Contents

• Common Requirements for Real-Time Multi-Agent Systems
  • Core Pattern
  • Core Functional Requirements
  • What’s Missing from Flink Today (Core Gaps)
• MVP Use Cases to Focus On
  • Initial Gaps to Address
• MVP Use Cases
  • Real-Time Inventory Rebalancing
  • Real-Time Supply Chain Management
  • Real-Time Product Personalization from Review Analysis
• Other Use Cases
  • Real-Time Lead Management
  • Real-Time Insurance Claims Processing
  • Real-Time Grocery Catalog Maintenance
  • Real-Time Customer Support Ticket Management
  • Real-Time Medical Bill Filings
  • Real-Time Loan Underwriting
  • Real-Time Transportation System Management
  • Real-Time IoT Device Monitoring and Autonomous Recovery
• Backlog of Use Cases

Common Requirements for Real-Time Multi-Agent Systems

Whether classifying insurance claims, qualifying leads, or rebalancing inventory, these systems depend on a shared set of capabilities that enable agents to operate autonomously, coordinate asynchronously, and adapt continuously.

Core Pattern

Most continuous, asynchronous, agents for business use cases follow a similar pattern. They require:

• Heterogenous inputs
• Streaming joins and other data processing-related operations across inputs
• An event-based trigger based on the outputs of the streaming joins that serves as input to the agent
• The result of the agent fanning out to multiple systems

Use cases that follow this pattern have a strong requirement for combining data processing and agentic work. This makes Flink the logical choice for these types of agents. Otherwise you’d have to run and manage two separate systems, one for data processing and one for running the agent, moving data back and forth between the two.

Core Functional Requirements

The following capabilities are consistently required to support MAS across use cases:

• Model Inference Support: Ability to call LLMs, classifiers, or other models for summarization, extraction, classification, and decision-making.
• Agent Tooling Framework: Support for calling structured APIs, third-party SaaS connectors, or internal tools, with the LLM dynamically choosing which tool to invoke based on context. Used for dynamic data gathering of context or taking actions.
• Semantic Search for Context Enrichment: Retrieve relevant documents, past cases, or unstructured content using embeddings or similarity search to augment agent reasoning.
• State Management: Retain entity-level memory (e.g., lead, claim, SKU) across steps and across the bounds of multiple agents.
• Agent Coordination: Route tasks between agents dynamically based on triage outcomes, model confidence, or other signals.
• Branching Logic: Support conditional logic and decision trees (e.g., escalate complex claims, retry failed enrichments).
• Parallel Execution: Enable agents to process independent aspects of the same problem space concurrently (e.g., policy verification and image assessment).
• Aggregation Support: Combine outputs from multiple agents to form a complete decision or final record (e.g., merge metadata, resolve conflicts).
• Replayability: Reprocess historical events for debugging and auditing with visibility into each agent step.
• Traceable Agent Actions: Structured logs of each agent’s reasoning, decisions, tool calls, and outputs, critical for observability and debugging.
• Local Testing and Validation: Simulate agent behavior locally with mocks, test data, or fixed prompts without deploying full Flink jobs.
• Feedback Learning with Human-in-the-Loop: Capture human feedback (e.g., edits, overrides, rejections) and use it to improve prompts, workflows, or decision policies over time.
• Agent Self-Reflection and Output Evaluation: Support for single-agent reflection or multi-agent critique workflows, where one agent assesses or refines the outputs of another. Enables prompt tuning, response validation, and confidence scoring in complex reasoning tasks.
• Multimodal Input Support (when relevant): Process and reason over mixed formats, text, images, PDFs, tabular data.
• LLM Inference Call Caching: Provide machinery to encode the (application-specific) means in which inference calls are equivalent to provide a cached response that is deemed acceptable through previous reinforcement to save time and expense of redundant LLM calls.

What’s Missing from Flink Today (Core Gaps)

While Flink provides a robust foundation for real-time stream processing and stateful computation, it lacks key primitives required to support multi-agent systems out of the box:

• Model Inference: No native support for calling external LLMs or other models.
• Agent Tooling Framework: No abstraction to register tools or allow an LLM to dynamically select and invoke them based on the agent’s context.
• Semantic Search Integration: No built-in support for embedding-based retrieval or vector search integration.
• Inter-Agent Context Sharing: While individual Flink jobs (acting as agents) can manage their internal state and short-term memory using Flink's native state management capabilities, a multi-agent system often requires agents to access and contribute to a shared understanding of the world or a common context. When each agent is a separate Flink job, there's no out-of-the-box Flink mechanism that provides a distributed, mutable, and easily queryable shared context store accessible across these job boundaries. Kafka as a messaging service can fill this gap with compacted topics.
• Parallel Agent Execution: No native concept of agent-level fanout. Developers must coordinate parallel execution using Kafka topic fanout and multiple Flink consumers. Common multi-agent system operations patterns like orchestrator-worker, hierarchical, blackboard, and market should be supported with abstractions that enable event-driven implementations without forcing developers to engage in the details of modeling topics and message flow.
• Multi-Agent Aggregation: It’s difficult for an aggregator to know for sure when all necessary pieces of information have been contributed. Combining results from concurrent agents requires a standard semantics for custom keyed joins, windows, or process functions; there's no standard mechanism for merging intermediate outputs.
• Deterministic Replay: While Flink + Kafka enable general reprocessing, there’s no first-class support for replaying an entity’s journey through a multi-agent workflow, inspecting each step’s decisions, state, and outputs.
  Traceable Agent Actions: No built-in support for structured, semantically rich logging of agent decisions, tool usage, or reasoning steps.
• Local Testing and Validation: No utilities for mocking tools, injecting test data, or simulating MAS interactions without deploying a full Flink pipeline.
• Feedback Learning with Human-in-the-Loop: No built-in pattern or interface for capturing reviewer edits or overrides and feeding them back into agent workflows for training or prompt refinement.
• Multimodal Model Support: No integrations for combining text, image, and structured data processing, such as OCR, vision models, or multimodal LLMs.

MVP Use Cases to Focus On

To ground the MVP in concrete, high-impact applications, we suggest focusing on three representative use cases: Product Personalization/Review Analysis, Supply Chain Management, and Real-Time Inventory Rebalancing. Details on these use cases are below.

All three use cases prominently feature a need for:

• Real-time, high-volume event stream processing: Ingesting and joining data from multiple sources like product reviews and catalog metadata, sales transactions, inventory logs, and supplier feeds.
• Upstream and downstream integration: In some cases (e.g., Product Review Analysis), incoming data must be pre-processed, merged, or grouped before being fed into agent workflows. In others (e.g., Inventory Rebalancing), agent outputs must be routed to external logistics or ERP systems for execution.
• Advanced AI/LLM capabilities: For tasks such as natural language understanding in customer reviews, demand forecasting in supply chains, and decision-making logic in inventory rebalancing.
• Complex Event Processing (CEP): To detect patterns, anomalies, and critical events (e.g., stockouts, demand surges, supply disruptions).
• Robust inter-agent context sharing: Ensuring that all agents in a workflow operate with consistent and up-to-date information.
• Agent tooling and autonomous execution: Agents need to interact with external systems (CRMs, ERPs, logistics platforms) and execute decisions.
• Traceability, deterministic replay, and human oversight: Crucial for debugging, optimization, compliance, and integrating human expertise.

Initial Gaps to Address

To address the bulk of the functionalities described in Customer Support Ticket Management, Supply Chain Management, and Real-time Inventory Rebalancing, the minimal set of critical gaps that need to be addressed would focus on enabling intelligent, collaborative, and interactive agents that can be reliably developed and operated.

Here is a minimal set of five critical gaps:

1. Model Inference:

• Why: This is fundamental. All three use cases heavily rely on AI models for their core logic.

2. Agent Tooling Framework:

• Why: Agents in these use cases need to interact with a variety of external systems and data sources (CRMs, ERPs, supplier APIs, databases, communication platforms, documentation). A unified framework for agents to select, invoke, and manage these "tools" is crucial for them to gather necessary information and execute actions in the real world.

3. Inter-Agent Context Sharing:

• Why: The described solutions are multi-agent systems where workflows involve several specialized agents passing information and building upon each other's work. Consistent, reliable, and efficient context sharing (e.g., customer history, enriched lead data, current inventory status, intermediate supply chain calculations) is the bedrock of their collaboration.

4. Deterministic Replay:

• Why: The capability to re-execute a past workflow with the exact same inputs and conditions to reproduce a specific behavior or outcome. This is invaluable for in-depth root cause analysis of failures or unexpected results, and for reliably A/B testing changes to agent logic using historical scenarios.

5. Traceability:

• Why: The ability to log and inspect the sequence of actions, decisions, and data transformations made by each agent in a workflow. This is crucial for understanding why a system produced a particular outcome, which is vital for debugging, auditing (e.g., in supply chain compliance or customer support dispute resolution), and building trust.

6. Local Testing:

• Why: Provides developers with the ability to simulate and test the end-to-end multi-agent system, or its components, in an isolated local environment. This accelerates development cycles, facilitates easier debugging of agent interactions, and reduces the risk of deploying faulty logic to production. The provided use cases explicitly highlight this as a critical gap.

Addressing these gaps would provide the foundational capabilities to build, deploy, and manage the core intelligent, collaborative, and interactive aspects of the described multi-agent systems for Customer Support, Supply Chain Management, and Real-time Inventory Rebalancing. While other gaps like "Human Review Loop with Feedback Learning" are also very important for full operational maturity and optimization, this set represents the most critical features.

MVP Use Cases

Real-Time Inventory Rebalancing

Retailers with multiple locations and sales channels often face stock imbalances: high demand at one store or region, and overstock in another. Traditionally, these are addressed manually or with batch-based rules, which fail to react quickly to real-time fluctuations. A multi-agent system can help detect imbalances early and trigger rebalancing actions dynamically based on current sales, inventory, and supply conditions.

Modular Agent Design

We can decompose this into the following set of agents:

• Sales Monitoring Agent: Listens to sales data across stores and channels to detect surges in demand or anomalies in product velocity.
• Inventory Monitor Agent: Tracks real-time inventory across stores, warehouses, and fulfillment centers. Detects understock and overstock situations.
• Supply Checker Agent: Pulls data from vendor APIs and internal supply chain systems to determine feasibility of replenishment or lead times.
• Rebalance Decision Agent: Combines insights from the other agents to determine whether to trigger restock, reallocate from nearby stores, or delay fulfillment.
• Logistics Execution Agent: Issues transfer or purchase orders, schedules pickups, and updates inventory systems accordingly.
• Outcome Tracker Agent: Monitors the execution of decisions and adjusts future actions based on delivery success, sales continuation, and customer satisfaction signals.

Unique Emphasis or Requirements

• Multiple input streams (sales, inventory, supply chain)
• CEP-style event pattern detection for surges and stockouts
• Conditional logic to choose between restock vs. transfer
• Aggregation of product metrics per region or category
• Need for autonomous execution agents (e.g., order placement)

Critical Gaps in Flink

• Model Inference
• Agent Tooling Framework
• Parallel Agent Execution
• Multi-Agent Aggregation
• Inter-Agent Context Sharing
• Deterministic Replay
• Traceability and Local Testing

Real-Time Supply Chain Management

Modern supply chains face constant pressure from unpredictable disruptions—supplier delays, inventory imbalances, fluctuating demand, and transportation bottlenecks. Businesses struggle to respond quickly because decisions are distributed across siloed teams and systems, and often rely on outdated data.

A multi-agent system (MAS) can help by transforming fragmented workflows into a coordinated, real-time network of specialized agents. These agents ingest live signals from suppliers, inventory systems, and logistics providers, then collaborate to rebalance stock, reroute shipments, update forecasts, or trigger replenishments.

Modular Agent Design

We can decompose this into the following set of agents:

• Demand Forecast Agent: Continuously ingests sales and market data to predict future demand across products and regions. Uses time-series models and LLM-based reasoning for unstructured signals.
• Procurement Agent: Dynamically selects and negotiates with suppliers based on forecasts, contract terms, pricing, lead times, and risk scores.
• Production Planning Agent: Plans production schedules based on available capacity, raw materials, and real-time demand. Adjusts to delays or shifting priorities.
• Inventory Management Agent: Monitors stock levels across warehouses and fulfillment nodes. Triggers restocks, reallocations, and slow-mover mitigation strategies.
• Logistics Optimization Agent: Selects optimal transportation routes and carriers based on cost, emissions, and delivery constraints. Reacts in real time to disruptions.
• Disruption Response Agent: Scans external signals (e.g., weather, strikes, port closures) and initiates re-planning workflows when disruptions are detected.
• Sustainability Agent (optional): Scores actions based on environmental metrics such as emissions and waste. Recommends lower-impact alternatives.
• Returns & Reverse Logistics Agent (optional): Coordinates returns, recycling, and repurposing workflows across partners and fulfillment centers.

Unique Emphasis or Requirements

• Ingestion from diverse event streams: sales, inventory, transportation, weather, and supplier APIs
• Context-aware planning with memory sharing across agents
• Joint reasoning over structured data and unstructured signals (e.g., supplier risk profiles)
• Feedback loops to refine decisions based on actual execution outcomes
• CEP-style anomaly detection to trigger cascading updates across agents

Critical Gaps in Flink

• Model Inference
• Agent Tooling Framework
• Semantic Search
• Parallel Agent Execution
• Multi-Agent Aggregation
• Inter-Agent Context Sharing
• Deterministic Replay
• Traceability and Local Testing

Real-Time Product Personalization from Review Analysis

E-commerce companies collect massive volumes of product reviews, but most of that data goes unused beyond basic star ratings or sentiment averages. The core challenges are:

1. Extracting structured signals from unstructured text (e.g., common likes/dislikes, recurring issues)
2. Acting on those insights to improve customer experience and drive engagement

A multi-agent system can transform this passive feedback into a real-time, event-driven loop—where reviews trigger downstream actions like product changes or personalized marketing.

Modular Agent Design

We can decompose this into the following sequence of agents and dataflow steps:

• Metadata Join (Table API): Join consumer reviews with product metadata such as category, brand, and price segment.
• Sentiment & Feature Extraction Agent: Uses LLMs to:
  • Estimate a 0–5 sentiment score based on review text
  • Extract 0–3 like/dislike reasons per review
• Aggregation (DataStream API): For each product:
  • Aggregate review-level insights
  • Compute sentiment distribution
  • Collect union set of like/dislike reasons
• Product Summary Agent: Summarizes the top 3 like/dislike reasons for each product.
• Customer Risk Detection Agent: Identifies customers with negative experiences or recurring dissatisfaction patterns.
• Campaign Design Agent: Crafts personalized engagement strategies—e.g., apology emails, recommendations, or discounts—based on review history and sentiment trends.
• Downstream Integration: Routes results to dashboards (for product teams) and marketing systems (for automated campaigns).

Unique Emphasis or Requirements

• Joins across structured product data and unstructured reviews
• Deduplication of feedback based on semantic similarity
• Reliable LLM output parsing, validation, and fallback mechanisms
• Multi-agent memory and coordination to pass user insights downstream
• Dual output routing to both operational tools (e.g., CRM) and analytical platforms

Critical Gaps in Flink

• Integration between Table API and DataStream API for agent workflows
• LLM Output Structuring and Validation
• Semantic Deduplication Framework
• Context Sharing Across Agent Steps
• Downstream Output Routing Mechanisms
• Agent Tooling and Lifecycle Orchestration

Other Use Cases

Real-Time Lead Management

In B2B sales, lead qualification and outreach are time-consuming, multi-step workflows. SDRs must monitor incoming leads, enrich them with CRM and third-party data, score them based on fit and intent, and follow up across channels like email, LinkedIn, or webchat. These tasks require constant context-switching and personalization, yet most teams still rely on rigid automation or manual processes.

A multi-agent system can decompose this asynchronous pipeline into independent but coordinated agents that handle ingestion, enrichment, scoring, planning, and execution.

Modular Agent Design

We can decompose this into the following set of agents:

• Lead Intake Agent: Listens to new leads from web forms, campaigns, or product usage events.
• Enrichment Agent: Augments the lead with CRM, firmographic, and intent data via APIs or internal services.
• Scoring Agent: Classifies the lead using predictive models (fit score, engagement score, etc.).
• Outreach Planner Agent: Determines the next best action (e.g., email, human handoff, drop) based on scoring and historical outcomes.
• Execution Agent: Triggers outreach (e.g., personalized email, webchat) and logs activity in CRM.

These agents communicate through event streams and shared state, enabling asynchronous but coordinated execution.

Unique Emphasis or Requirements

• Continuous ingestion from event streams (e.g., CRM, product signals, web forms)
• Frequent use of LLMs for personalization, summarization, and planning
• Inter-agent context transfer between enrichment, scoring, and planning
• Deterministic replay for debugging and SDR strategy optimization
• Personalization at scale using dynamic tool calls and data fusion

Critical Gaps in Flink

• Model Inference
  • No native mechanism for the Scoring Agent to invoke predictive models or for the Planner Agent to use LLMs for real-time decision-making
• Agent Tooling Framework
  • Enrichment requires complex integrations with APIs (e.g., CRM, firmographics) and lacks a unified interface for dynamic tool invocation by agents
• Inter-Agent Context Sharing
  • Context (e.g., contact info, enrichment data, scores) must persist and flow across agents, which is difficult to orchestrate today
• Deterministic Replay
  • SDR workflows can’t be easily audited or A/B tested due to lack of replayable execution across agent steps
• Traceability and Local Testing
  • Developers can’t easily simulate and debug the multi-agent pipeline without deploying into a full Flink environment

Real-Time Insurance Claims Processing

Insurance claims processing is complex, multi-step, and often bottlenecked by manual review and coordination. A single claim may involve gathering documents, verifying policies, analyzing evidence (photos, videos, logs), checking for fraud, and managing claimant communication.

A multi-agent system (MAS) can break this process into interoperable agents that automate and coordinate discrete tasks—from intake through final decisioning.

Modular Agent Design

We can decompose this into the following set of agents:

• Claims Intake Agent: Extracts structured data from forms, PDFs, scanned images, or emails using OCR and LLMs. Normalizes values like policy numbers, dates, and damage types.
• Triage Agent: Classifies claims by complexity or urgency. Routes to auto-approval, human review, or deeper analysis paths.
• Vision Agent: Analyzes visual evidence (e.g., damage photos/videos), extracts metadata, assesses severity, and verifies alignment with written descriptions.
• Policy Verification Agent: Validates coverage and policy terms against incident descriptions, checking inclusions/exclusions and limits.
• Risk Assessor Agent: Correlates incident with external signals (e.g., NOAA weather, prior incidents) to assess risk and mitigation, assigning scores.
• Decision Agent: Recommends settlement (approval, denial, partial) based on policy, risk, and documentation. Computes payout after deductibles.
• Document Generator Agent: Drafts EOBs, settlement letters, and internal audit/compliance documentation.
• Feedback Loop Agent: Monitors downstream outcomes (e.g., disputes, reversals), flags issues, and suggests workflow/model updates.

Unique Emphasis or Requirements

• Multimodal inputs: OCR, image models, and LLMs working across forms, photos, and documents
• Policy-aware logic combining structured rules with model reasoning
• Linking context across agents (e.g., damage evidence ↔ policy terms)
• Audit-ready documentation for compliance and transparency
• Closed feedback loops from appeals and rejections into agent improvement cycles

Critical Gaps in Flink

• Model Inference
  • No native support for LLMs or structured output extraction needed by intake and decision agents
• Multimodal Model Support
  • Limits Claims Intake Agent (OCR) and Vision Agent (image/video analysis) functionality
• Agent Tooling Framework
  • Lacks unified interfaces for external tool invocation (e.g., policy systems, weather APIs)
• Inter-Agent Context Sharing
  • Makes it hard to maintain a consistent evolving claim state across agents (e.g., from intake to decision)
• Parallel Agent Execution & Multi-Agent Aggregation
  • Hinders concurrent workflows (e.g., vision and policy validation) and efficient merging of results
• Deterministic Replay
  • Prevents repeatable audits or A/B testing of decisions
• Traceability and Local Testing
  • Limits developers’ ability to simulate or debug full claim flows locally

Real-Time Grocery Catalog Maintenance

Maintaining a high-quality grocery catalog at scale requires ingesting messy, inconsistent product data from thousands of retailers—each with different formats, naming conventions, and data quality levels. The objective is to transform this fragmented input into a unified, structured catalog suitable for search, recommendations, advertising, and analytics.

A multi-agent system can orchestrate the cleaning, normalization, tagging, and merging of product data in a high-throughput, asynchronous pipeline.

Modular Agent Design

We can break this down into a set of specialized, coordinated agents:

• Ingestion Agent: Listens for new catalog updates from external retailers and parses raw data into structured records.
• Normalization Agent: Standardizes product fields (e.g., names, sizes) using LLMs and regex-based transformations
  • Example: “Strawberries 1LB”, “1-lb strawberries”, and “Strawberries - 16 oz” become a consistent format
• Deduplication Agent: Detects and merges duplicate or near-duplicate items across vendors and formats.
• Categorization Agent: Classifies products into a unified taxonomy (e.g., produce > berries > strawberries) using LLMs or traditional classifiers.
• Tagging Agent: Enriches items with searchable and ad-targetable attributes such as “organic,” “gluten-free,” “kid-friendly,” or “high-protein.”
• Merge Agent: Constructs the canonical product record by aggregating metadata from all other agents.

Unique Emphasis or Requirements

• High-throughput ingestion across thousands of vendors
• LLM-based normalization, classification, and tagging
• Duplicate detection and canonical record construction
• Parallel enrichment workflows (e.g., tagging and categorization)
• Unified metadata view for each product

Critical Gaps in Flink

• Model Inference
  • Needed for field normalization, classification, and tag generation
• Agent Tooling Framework
  • Complex tool orchestration for enrichment agents (e.g., taxonomy APIs, tag classifiers)
• Parallel Agent Execution
  • Enrichment steps like categorization and tagging should run concurrently post-normalization
• Multi-Agent Aggregation
  • Merge Agent must combine inputs from deduplication, tagging, and classification into a single product record
• Inter-Agent Context Sharing
  • Requires consistent access to evolving product state across normalization, enrichment, and merge steps
• Deterministic Replay
  • Enables reprocessing for updates or debugging canonicalization logic
• Traceability and Local Testing
  • Difficult to simulate end-to-end flows and test enrichment/debugging locally

Real-Time Customer Support Ticket Management

Customer support teams face a constant influx of tickets—ranging from billing issues to technical troubleshooting—under tight time constraints and high customer expectations. Creating personalized, policy-aligned responses requires searching internal docs, referencing customer history, and maintaining consistent tone and quality.

A multi-agent system can augment this process by automatically triaging tickets, retrieving relevant context, and generating first-draft responses using LLMs. Human agents can review, approve, or revise these drafts—accelerating response times while maintaining control, consistency, and traceability.

Modular Agent Design

We can decompose this into the following set of agents:

• Ticket Intake Agent: Listens for new tickets from email, chat, or support forms. Extracts metadata such as customer ID, issue category, and urgency. May use LLMs or classification models for triage.
• Context Retrieval Agent: Pulls relevant data including customer history, past tickets, product logs, known issues, and internal documentation.
• Response Drafting Agent: Uses an LLM to compose a first-draft response using retrieved context and predefined tone/policy guidelines (e.g., empathy, refund policy).
• Review Coordination Agent: Presents the draft to a human agent for edits, approval, or rejection. Tracks override frequency and gathers structured feedback.
• Feedback Learning Agent (optional): Monitors edits and outcomes (e.g., CSAT, reopen rate) to improve prompts, retrieval, or tool invocation over time.
• Audit & Escalation Agent (optional): Flags high-risk content (e.g., legal threats, account deletions) for mandatory escalation or additional review.

Unique Emphasis or Requirements

• Real-time ingestion of support tickets from multiple channels (email, chat, web forms)
• LLMs for ticket classification, triage, and response generation
• Retrieval-augmented generation grounded in customer history and documentation
• Human-in-the-loop review with feedback collection
• Optional escalation for sensitive or high-risk interactions

Critical Gaps in Flink

• Model Inference
  • Needed for classification, triage, and response generation using LLMs
• Agent Tooling Framework
  • Required to invoke internal tools and APIs (e.g., knowledge bases, customer systems) from agents
• Semantic Search
  • Essential for the Context Retrieval Agent to surface relevant support history and documentation
• Inter-Agent Context Sharing
  • Enables consistent access to evolving ticket state and retrieved artifacts across agents
• Deterministic Replay
  • Supports auditability, debugging, and experimentation with updated models/prompts
• Human Review Loop with Feedback Learning
  • Requires coordination between agents to capture, evaluate, and learn from human overrides
• Traceability and Local Testing
  • Difficult to simulate the full end-to-end agent pipeline for development and QA

Real-Time Medical Bill Filings

Filing medical claims is often slow, error-prone, and highly manual. It involves extracting information from clinical notes, validating data against payer-specific rules, and submitting claims through external systems. Errors at any stage lead to delays, denials, and lost revenue.

A multi-agent system can streamline this process—automating extraction, validation, submission, and feedback learning to reduce rejection rates and speed up reimbursements.

Modular Agent Design

We can decompose this into the following set of agents:

• Intake Agent: Listens for billing events such as completed appointments or discharges. Parses structured and unstructured input from EHRs, PDFs, or clinical notes.
• Data Extraction Agent: Uses OCR and LLMs to extract relevant billing codes (CPT, ICD-10), procedures, medications, and visit metadata.
• Validation Agent: Cross-checks the extracted data against payer-specific requirements—ensuring required fields, valid code combinations, and eligibility alignment.
• Claim Generation Agent: Assembles a structured claim form with validated data, ready for digital submission.
• Submission & Tracking Agent: Sends claims to the appropriate payer or clearinghouse, tracks status, and flags rejections or follow-ups.
• Appeals or Correction Agent (optional): Generates corrected claims or appeals based on rejection reasons, reusing and adjusting prior data.
• Feedback Learning Agent (optional): Learns from submission outcomes to refine extraction logic, improve validation rules, or adjust prompts.

Unique Emphasis or Requirements

• Real-time ingestion of billing events from EHR and hospital systems
• LLMs for extracting codes from free-text clinical records
• Complex validation against payer-specific, evolving rule sets
• Structured document generation for claims
• External system integration for submission and tracking
• Optional human-in-the-loop review and feedback learning from denials

Critical Gaps in Flink

• Model Inference
  • Needed for OCR + LLM-based code extraction from unstructured notes
• Agent Tooling Framework
  • Required for integrating with payer APIs and clinical data systems
• Inter-Agent Context Sharing
  • Must maintain consistent access to patient visit data across agents
• Deterministic Replay
  • Enables root-cause analysis of rejections and safe pipeline debugging
• Human Review Loop with Feedback Learning
  • Coordination of edits and iterative learning from claim denials is difficult
• Traceability and Local Testing
  • Hard to simulate full billing flows across multiple agents for dev and QA

Real-Time Loan Underwriting

Loan underwriting requires evaluating a borrower’s financial profile, verifying documents, assessing risk, and generating compliant decisions—all under strict regulatory constraints. The process is often manual, slow, and prone to inconsistencies.

A multi-agent system can streamline and modularize underwriting: separating ingestion, verification, risk analysis, and communication into coordinated, auditable steps.

Modular Agent Design

We can decompose this into the following set of agents:

• Application Intake Agent: Ingests structured loan applications and uploads supporting documents.
• Document Verification Agent: Validates IDs, paystubs, tax forms, and other materials using OCR and rule-based checks.
• Credit & Risk Agent: Pulls credit reports and fraud data, evaluates debt-to-income ratio, and calculates risk scores.
• Decision Agent: Summarizes the application and recommends approval, denial, or counteroffer based on policy and risk thresholds.
• Letter Generation Agent: Crafts personalized approval or denial letters explaining rationale in compliance with regulations.

Unique Emphasis or Requirements

• Document processing with OCR and LLMs
• Real-time credit evaluation and fraud detection
• Compliance-focused auditability and decision explainability
• Personalized communication based on structured + unstructured inputs

Critical Gaps in Flink

• Model Inference
  • Needed for risk scoring, document extraction, and personalized content generation
• Agent Tooling Framework
  • Required for interfacing with credit bureaus, employment verification, and fraud detection APIs
• Inter-Agent Context Sharing
  • Ensures risk agents and letter generators access consistent application state
• Deterministic Replay
  • Supports debugging of underwriting logic and A/B testing of decision thresholds
• Human Review Loop with Feedback Learning
  • Improves model logic and decisions based on manual overrides or policy updates
• Traceability and Local Testing
  • Enables simulation of entire underwriting pipelines during development

Real-Time IoT Device Monitoring and Autonomous Recovery

In large-scale IoT environments—such as manufacturing floors, smart cities, energy grids, and logistics fleets—device failures can lead to service disruptions, safety risks, and revenue loss. These systems involve thousands of sensors and actuators generating continuous telemetry.

Traditional approaches rely on reactive alerts and manual intervention. A multi-agent system (MAS) can enable autonomous detection, triage, and recovery workflows, reducing mean time to repair (MTTR) and increasing system resilience.

Modular Agent Design

We can decompose this into the following set of agents:

• Telemetry Ingestion Agent: Continuously processes telemetry from sensors and gateways. Filters noise, detects anomalies (e.g., signal loss, battery drop, overheating), and applies failure signatures.
• Failure Classification Agent: Uses LLMs or classifiers to determine severity and cause (e.g., recoverable vs. hardware fault).
• Context Retrieval Agent: Pulls metadata such as device type, location, config history, firmware version, and similar past failures.
• Remediation Planning Agent: Determines the optimal recovery step (e.g., restart, rollback, reconfig) based on context and historical resolution data.
• Execution Agent: Applies remediation via device management systems and records results.
• Escalation & Notification Agent: Alerts operators on unresolved or critical failures. Summarizes attempted actions and suggests alternatives.
• Learning Agent (optional): Analyzes patterns across historical failures, operator feedback, and resolution outcomes to improve future decisions.

Unique Emphasis or Requirements

• High-velocity ingestion from thousands of edge devices
• Robust anomaly detection over noisy time-series data
• History- and policy-aware recovery planning
• Human-in-the-loop fallback with traceability
• RCA and trend analytics for device health over time
• Semantic search for playbook retrieval and incident similarity
• Optional: predictive alerts before failure materializes

Critical Gaps in Flink

• Model Inference
  • Needed for anomaly detection, failure classification, and planning steps
• Agent Tooling Framework
  • Required for integration with IoT management systems and external APIs
• Semantic Search
  • Enables retrieval of past recovery strategies or similar failure cases
• Parallel Agent Execution
  • Allows classification, context gathering, and remediation planning to run concurrently
• Multi-Agent Aggregation
  • Combines telemetry, device context, and recovery results into unified incident records
• Inter-Agent Context Sharing
  • Maintains consistent state of each incident across agents in real time
• Deterministic Replay
  • Supports root cause analysis, auditing, and testability of recovery flows
• Traceability and Local Testing
  • Enables safe simulation of complex recovery paths across agents
• LLM Call Caching
  • Reduces redundant LLM usage for repeated failures with similar characteristics

Real-Time IoT Device Monitoring and Autonomous Recovery

In large-scale IoT environments—such as manufacturing floors, smart cities, energy grids, and logistics fleets—device failures can lead to service disruptions, safety risks, and revenue loss. These systems involve thousands of sensors and actuators generating continuous telemetry.

Traditional approaches rely on reactive alerts and manual intervention. A multi-agent system (MAS) can enable autonomous detection, triage, and recovery workflows, reducing mean time to repair (MTTR) and increasing system resilience.

Modular Agent Design

We can decompose this into the following set of agents:

• Telemetry Ingestion Agent: Continuously processes telemetry from sensors and gateways. Filters noise, detects anomalies (e.g., signal loss, battery drop, overheating), and applies failure signatures.
• Failure Classification Agent: Uses LLMs or classifiers to determine severity and cause (e.g., recoverable vs. hardware fault).
• Context Retrieval Agent: Pulls metadata such as device type, location, config history, firmware version, and similar past failures.
• Remediation Planning Agent: Determines the optimal recovery step (e.g., restart, rollback, reconfig) based on context and historical resolution data.
• Execution Agent: Applies remediation via device management systems and records results.
• Escalation & Notification Agent: Alerts operators on unresolved or critical failures. Summarizes attempted actions and suggests alternatives.
• Learning Agent (optional): Analyzes patterns across historical failures, operator feedback, and resolution outcomes to improve future decisions.

Unique Emphasis or Requirements

• High-velocity ingestion from thousands of edge devices
• Robust anomaly detection over noisy time-series data
• History- and policy-aware recovery planning
• Human-in-the-loop fallback with traceability
• RCA and trend analytics for device health over time
• Semantic search for playbook retrieval and incident similarity
• Optional: predictive alerts before failure materializes

Critical Gaps in Flink

• Model Inference
  • Needed for anomaly detection, failure classification, and planning steps
• Agent Tooling Framework
  • Required for integration with IoT management systems and external APIs
• Semantic Search
  • Enables retrieval of past recovery strategies or similar failure cases
• Parallel Agent Execution
  • Allows classification, context gathering, and remediation planning to run concurrently
• Multi-Agent Aggregation
  • Combines telemetry, device context, and recovery results into unified incident records
• Inter-Agent Context Sharing
  • Maintains consistent state of each incident across agents in real time
• Deterministic Replay
  • Supports root cause analysis, auditing, and testability of recovery flows
• Traceability and Local Testing
  • Enables safe simulation of complex recovery paths across agents
• LLM Call Caching
  • Reduces redundant LLM usage for repeated failures with similar characteristics

Real-Time IoT Device Monitoring and Autonomous Recovery

In large-scale IoT environments—such as manufacturing floors, smart cities, energy grids, and logistics fleets—device failures can lead to service disruptions, safety risks, and revenue loss. These systems involve thousands of sensors and actuators generating continuous telemetry.

Traditional approaches rely on reactive alerts and manual intervention. A multi-agent system (MAS) can enable autonomous detection, triage, and recovery workflows, reducing mean time to repair (MTTR) and increasing system resilience.

Modular Agent Design

We can decompose this into the following set of agents:

• Telemetry Ingestion Agent: Continuously processes telemetry from sensors and gateways. Filters noise, detects anomalies (e.g., signal loss, battery drop, overheating), and applies failure signatures.
• Failure Classification Agent: Uses LLMs or classifiers to determine severity and cause (e.g., recoverable vs. hardware fault).
• Context Retrieval Agent: Pulls metadata such as device type, location, config history, firmware version, and similar past failures.
• Remediation Planning Agent: Determines the optimal recovery step (e.g., restart, rollback, reconfig) based on context and historical resolution data.
• Execution Agent: Applies remediation via device management systems and records results.
• Escalation & Notification Agent: Alerts operators on unresolved or critical failures. Summarizes attempted actions and suggests alternatives.
• Learning Agent (optional): Analyzes patterns across historical failures, operator feedback, and resolution outcomes to improve future decisions.

Unique Emphasis or Requirements

• High-velocity ingestion from thousands of edge devices
• Robust anomaly detection over noisy time-series data
• History- and policy-aware recovery planning
• Human-in-the-loop fallback with traceability
• RCA and trend analytics for device health over time
• Semantic search for playbook retrieval and incident similarity
• Optional: predictive alerts before failure materializes

Critical Gaps in Flink

• Model Inference
  • Needed for anomaly detection, failure classification, and planning steps
• Agent Tooling Framework
  • Required for integration with IoT management systems and external APIs
• Semantic Search
  • Enables retrieval of past recovery strategies or similar failure cases
• Parallel Agent Execution
  • Allows classification, context gathering, and remediation planning to run concurrently
• Multi-Agent Aggregation
  • Combines telemetry, device context, and recovery results into unified incident records
• Inter-Agent Context Sharing
  • Maintains consistent state of each incident across agents in real time
• Deterministic Replay
  • Supports root cause analysis, auditing, and testability of recovery flows
• Traceability and Local Testing
  • Enables safe simulation of complex recovery paths across agents
• LLM Call Caching
  • Reduces redundant LLM usage for repeated failures with similar characteristics

Backlog of Use Cases

• RFP first draft completion
• Procurement order processing
• Inventory monitoring and restocking
• Workforce scheduling
• Custom warranty and returns processing
• Gap analysis on regulatory changes
• General advice and product recommendations for e-commerce
• Offer personalization or price optimization
• Call analysis and documentation for sales, financial advisors, etc.
• Audit optimization (e.g., energy companies automating safety audits)
• Camera intelligence monitoring (e.g., for security and self-driving cars)