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Before Onboarding Agent AI Developers: Know the Essential Data Streaming Skillsets that You Need

 

Our expertise in configuring automated data streaming workflows using technologies like Apache Kafka, Confluent, or AWS Kinesis is playing a critical role in bridging the gap between legacy systems and modern Generative AI (GenAI) applications. Below is a list of key challenges faced by our clients involving the implementation of Gen AI.

Challenges with Legacy Systems:

  1. Batch-based processing – Data is updated infrequently (daily, weekly)
  2. Data silos – Customer, operational, and transactional data is fragmented across systems
  3. High latency – Insights from data are delayed due to lack of real-time integration
  4. Inflexible architectures – Legacy systems can’t easily scale or connect to modern AI services
  5. Complex ETL pipelines – Heavy lift is needed to extract and prepare data for ML or GenAI models

Find Answers to Some Key Questions 

Why automated data streaming is essential for Agent AI deployment?

How automated data streaming platform helps overcome the challenges of legacy systems to implement Agent AI?

Some AI Agent applications deployed by Sun Technologies developers and testers that simplify forecasting

 

Find out if you have the key skillsets to integrate Agent AI Bots into your enterprise IT estate.

Discover how top U.S banks and finance institutions are leveraging our affordable Agent AI developer & testing services.

The following are some important skills that you need to implement fault-tolerant data streaming for Agent AI applications:

Tune message batching to handle heavy AI data loads
AI applications often deal with massive volumes of data in real time. Instead of sending messages one by one, group them into batches to cut down on network overhead. Kafka settings like linger.ms and batch-size can be adjusted to strike the right balance between throughput and latency.

Offload older AI data using tiered storage
Kafka isn’t built for keeping historical data forever. For large training datasets or long-term logs, consider using tiered storage like Kafka’s own Tiered Storage or external options like Hadoop or Amazon S3. This keeps Kafka lean and responsive without sacrificing access to older data.

Do feature engineering on the fly with Kafka Streams
Rather than preprocessing data before it hits your pipeline, use Kafka Streams or ksqlDB to transform, aggregate, or extract features in real time. This helps AI models — especially in fraud detection or recommendation systems — make faster decisions using the freshest data.

Keep an eye on latency to maintain AI performance
Real-time inference is only as good as the data feeding it. Lag anywhere in the pipeline can throw off results. Tools like OpenTelemetry or Prometheus can help monitor end-to-end latency, ensuring your AI models stay accurate and responsive.

Use topic compaction to retain the latest state for AI models
Many AI models need a running history or current state to make accurate predictions. Kafka’s log compaction feature lets you keep just the most recent value for each key, giving your models the up-to-date context they need—without bloating your storage.

How our automated data streaming helps overcome legacy challenges and implement Agent AI

✅ 1. Real-Time Data Integration

  • Stream data from legacy apps, databases, and sensors continuously.
  • Acts as a real-time data bus to unify disparate sources.
  • Example: Customer data from a mainframe + transactions from Oracle + app logs = single AI-ready stream.

✅ 2. Data Enrichment on the Fly

  • Stream processors (e.g., Kafka Streams, Flink) enrich data before it is consumed by Agent AI apps.
  • Allows Agent AI to generate contextual insights (e.g., real-time fraud detection or dynamic customer summaries).

✅ 3. Event-Driven Architectures for AI Triggers

  • Agent AI apps can respond to events (e.g., “Customer just called support” → trigger real-time case summary).
  • Enables proactive actions based on real-world events.

✅ 4. Bridging Legacy & Cloud-Native AI

  • Streams feed Agent AI models hosted on cloud platforms (OpenAI, AWS Bedrock, Vertex AI).
  • Acts as the “real-time layer” between slow legacy systems and fast AI services.

✅ 5. Low-Latency Inference Loops

  • With continuous data flow, Agent AI models can infer, respond, and learn faster.
  • Example: Chatbots summarizing support tickets as they are created instead of waiting for daily dumps.

Here is a real-world scenario showing how a data streaming platform helps overcome the challenges of legacy systems to implement Agent AI:

·         Industry: Federal Lending Institution

·         Goal: Real-time loan risk monitoring and generative report automation

·         Legacy Challenge: Data stored in mainframes, batch updates, siloed customer and loan data

·         Modernization Goal: Implement Agent AI to generate real-time loan risk summaries and alerts

BEFORE: Legacy-Only Setup

System

Limitation

COBOL-based mainframes

Hard to integrate, batch updates only

Oracle DB for loan info

Data silos between risk, loan, and customer

Manual compliance reports

Time-consuming, prone to human error

No real-time decisioning

Risk reviews done weekly or monthly

AFTER: Introducing Kafka + GenAI

Step 1: Real-Time Data Streaming

  • Apache Kafka ingests loan application updates, risk parameters, and transaction data in real time from mainframes and Oracle databases using CDC (Change Data Capture).
  • Streams are unified into a centralized platform.

Step 2: Enriched Event Streams

  • Kafka Streams enriches raw data:
    • Combines customer behavior with loan activity
    • Calculates preliminary risk scores dynamically

Step 3: Agent AI Integration

  • An Agent AI model (hosted on Azure OpenAI or AWS Bedrock) consumes the enriched streams to perform the following:
    • Generate automated loan summaries for underwriters in natural language

“Borrower XYZ shows a 15% increase in risk due to late credit card payments and recent job loss.”

    • Produce regulatory compliance narratives for auditors
    • Suggest next actions (e.g., escalate, approve with conditions, flag for review)

Step 4: Real-Time Alerts & Dashboards

  • Kafka topics feed directly into BI dashboards and risk monitoring tools.
  • Compliance officers get Agent AI generated alerts such as the following:

“Loan portfolio for Midwest Region exceeding risk threshold due to rising delinquencies.”

 Business Outcomes:

Impact Area

Benefit

Risk Evaluation

Now continuous vs. monthly; improves speed & accuracy

Compliance Reporting

Auto-generated reports save 70% effort

Integration

Real-time bridge from mainframes to GenAI-ready cloud

Decision Making

Underwriters get AI-generated loan summaries instantly

Why It Works:

Data Streaming Platforms like Kafka solve three major legacy challenges:

  • Latency: They turn slow batch pipelines into live, event-driven flows.
  • Siloes: They unify disconnected systems into a central data bus.
  • Inflexibility: They decouple legacy systems from modern Agent AI APIs and tools.

Summary: Value of Streaming in Agent AI

Capability

Benefit

Real-time data ingestion

Keeps GenAI models fresh and relevant

Seamless legacy integration

Reduces data pipeline complexity

Event-driven architecture

Powers proactive Agent AI interactions

Scalable data processing

Enables AI on high-volume, high-velocity data

Lower AI deployment latency

Immediate insights from newly generated data

Below are a few use cases of forecasting simplified by the used of Agent AI Bots deployed by Sun Technologies:  

 

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Book your free consultation to get purpose-driven implementation roadmap of Agent AI integration with ROI and savings estimates from our experts.

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