Category: Intermediate (200)

Banking, financial services, and insurance (BFSI) institutions need AI systems that can act autonomously at market speed, but autonomy without governance is a nonstarter in an industry defined by regulatory intensity and operational risk. In this post, Apexon and AWS present an architecture-first approach to operationalizing agentic AI on AWS, with governance built in from the start across three layers: intelligence, orchestration and execution, and governance, security, and audit. Learn how governed agentic patterns apply to core BFSI workflows like customer onboarding, credit processing, and payments exception handling, enabling autonomy at scale while keeping every action traceable and auditable.

AI agents authenticate, hold permissions, and act on data, yet most organizations manage them as application components rather than governed identities. Learn how Saviynt Zuma works with Amazon Bedrock AgentCore to govern agents as first-class identities, from registration and least-privilege provisioning through runtime authorization, certification, and deprovisioning. Security teams can then answer which agents exist, who owns them, and what they can access.

TEKsystems Global Services (TGS) helped a large media and entertainment enterprise transform their Amazon Connect Customer automated evaluation system from 66.67 percent accuracy to 100 percent. They developed a strategic classification framework that matches each evaluation question to the optimal automation method, either generative AI, conversational analytics categories, or contact metrics. This layered approach eliminated AI hallucinations, enabled full interaction coverage, and turned quality assurance from an unreliable manual process into a trusted, production-ready system that delivers actionable coaching insights at scale.

As generative AI adoption accelerates, the gap between deploying automation and making it trustworthy is becoming the critical differentiator. The next frontier of multi-modal insights and real-time coaching starts with the right foundation.

Autonomous AI agents are transforming financial services — but in regulated industries, capability alone doesn’t drive adoption. Trust does. In this post, Fenergo and AWS engineers walk through the architecture behind the Fenergo Agent Operating System (Agent OS): a governance-first layer built on Amazon Bedrock, Amazon DynamoDB, Amazon SNS, Amazon SQS, and Amazon Timestream for InfluxDB that makes every agent decision traceable, explainable, and audit-ready. Learn how to design agentic AI systems that answer the question regulators always ask: why?

A data migration consulting firm’s growth was limited by dependence on a small group of expert practitioners to execute complex validation workflows, creating bottlenecks that delayed client deliverables and constrained profitability. Mission Cloud and AWS built a production AI agent on Amazon Bedrock AgentCore that guides consultants through structured validation workflows and supports natural language SQL analysis, democratizing expertise across the entire team. New employees are onboarded significantly faster, and validation cycles that previously took weeks now complete in days.

F5 AI Guardrails and F5 AI Red Team deliver continuous adaptive security for AI applications on AWS Bedrock. This post introduces three capabilities for enterprise AI security: pre-deployment model evaluation, adversarial testing at scale, and adaptive runtime protection. Learn how these capabilities compress the gap between threat discovery and defense deployment from weeks to minutes.

If your RAG deployment has plateaued—struggling with cross-domain reasoning, noisy retrieval at scale, or a model that never truly learns your business—you’re not alone. In this post, we explore how 4MINDS’s continuous model adaptation on AWS moves beyond RAG’s stateless architecture by building enterprise knowledge directly into model parameters, enabling AI that improves with every interaction. Learn how this AWS Marketplace solution integrates with Amazon Bedrock and Amazon SageMaker to deliver domain-adapted models without the operational burden of traditional fine-tuning.

A major bottleneck for controlling Apache Spark costs or scaling analytics and AI workloads is Spark’s hard dependency on traditional CPU-only infrastructure. In this post, learn how DataPelago Accelerator for Spark closes that gap by seamlessly running your Spark workloads on heterogeneous hardware, delivering up to 10 times faster execution and up to 80% lower costs with zero code changes, no data migration, and no disruption to existing security configurations. See production results across social media, ecommerce, and financial services customers as you find out how to run a proof of concept on your own workloads.

Modern data architectures often rely on separate databases for transactions, analytics, and AI, leading to extract, transform, and load (ETL) latency; data inconsistency; and rising costs. OceanBase addresses these challenges with a unified multimodel architecture that converges online transaction processing (OLTP), online analytical processing (OLAP), and vector retrieval within a single engine on Amazon Web Services (AWS).

As enterprises deploy AI agents at scale, traditional identity governance designed for human users can’t keep pace. With 80% of organizations reporting unintended agent actions, the need for purpose-built agent identity security has never been greater. Learn how SailPoint’s Agent Identity Security (AIS), built on Amazon Bedrock AgentCore, extends proven governance principles to autonomous AI agents, enabling automatic discovery, least-privilege enforcement, and continuous access reviews without slowing innovation.