Category: Amazon Bedrock

OTTera, a white-label streaming service powering over 2,000 FAST channels, cut scheduling time by 60% by replacing manual workflows with AI agents built on Amazon Bedrock. In partnership with AI/R Compass UOL, OTTera modernized its core systems and deployed conversational AI that turns proprietary data into real-time scheduling intelligence, with projected Year 1 net profit exceeding $6.3 million*.

Agentic AI succeeds or fails on its data foundation, not the model behind it. Denodo and AWS give AI agents governed, real-time access to distributed data across hybrid, multi-cloud, and sovereign environments, without replication. Paired with Amazon SageMaker, Amazon Bedrock, and Amazon Quick, Denodo’s AI data layer delivers the active context agents need to reason accurately. Learn how a pan-African bank cut integration effort by up to 90%.

As agent estates grow from a handful of pilots to hundreds of agents in production, teams need to decide which agents to stop and which to promote. This post shows how Dataiku Agent Management works with Amazon Bedrock AgentCore and Amazon CloudWatch to discover, monitor, and control agents, using the signals AgentCore already emits.

Learn how Kaufman Rossin partnered with Fisent Technologies, Launchpad.io by Pegasystems, and AWS to transform manual Regulation E compliance testing into a scalable Outcomes-as-a-Service solution. The service combines deterministic workflow automation, applied generative AI, and cloud-native AWS infrastructure. It has processed more than 100,000 dispute cases across 30+ fintechs. Along the way, Kaufman Rossin achieved 70% faster testing and 50% fewer manual hours.

AI agents are making mainframe modernization programmable. This post walks through a data-first migration pattern that pairs Qlik Replicate’s log-based Change Data Capture (CDC) with the agentic AI of AWS Transform and Kiro. Using the strangler fig pattern and blue/green deployment, it migrates wave by wave, keeping the mainframe as system of record until each module is validated and the mainframe is finally decommissioned. Rising hardware costs, a shrinking COBOL and PL/I talent pool, batch-processing limits, and constrained innovation are driving organizations to make the move.

Managing AI costs is a top challenge for organizations scaling generative AI on AWS. This post shows how AWS CUR 2.0, Amazon S3, and AWS Trusted Advisor form the data foundation that New Relic Cloud Cost Intelligence builds on for Amazon Bedrock workloads. The result is token-level cost visibility inside the observability workflows engineering teams already use, shifting AI from opaque experimentation to disciplined, ROI-driven operations.

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.

The race to deploy agentic AI is outpacing the governance needed to put it into production – 74% of companies plan to deploy by 2027, yet only 21% have mature governance models. AWS launched GEAR (Governance Enabling Agentic Readiness) to close that gap, identifying partners whose governance accelerators compress months of setup into weeks and turn AI pilots into production-ready systems with measurable results.

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?