AVM at AWS re:Invent 2025: Leaning Into Agentic AI and Enterprise Autonomy

AVM Consulting participated in AWS re:Invent 2025 in Las Vegas, meeting with technology leaders across gaming, financial services, hospitality, and platform/observability domains.

This year’s re:Invent announcements centered on three major themes:

1. Agentic AI and Model Customization

• AWS introduced a new generation of Nova 2 foundation models, along with Nova Forge and Reinforcement Fine-Tuning, enabling organizations to build a model portfolio tailored to their workloads.
• Teams can continuously improve performance through structured feedback.
• Bedrock AgentCore provides agent deployments with built-in evaluation and quality controls.

Together, these features signal a shift toward production-grade agentic systems, not simple prompt-based automation.

2. Data & Infrastructure for Large-Scale AI

• Amazon S3 Vectors for large-scale vector search and retrieval.
• High-performance compute options such as Trainium3 UltraServers and Graviton5.
• AWS AI Factories to run fully managed AI hardware and services within enterprise data centers.

These building blocks make it easier to operate AI systems at scale while meeting regulatory, performance, and cost requirements.

3. Security & Observability as First-Class AI Concerns

• AWS Security Agent for secure-by-design application development.
• GuardDuty Extended Threat Detection for deeper multi-stage attack correlation.
• IAM Policy Autopilot to help teams generate safe, least-privilege IAM policies.
• CloudWatch unified data management and analytics for operational, security, and compliance telemetry.

These changes reflect AWS’s move toward a more AI-aware security and observability model.

How This Aligns With AVM’s Vision

These announcements reinforce AVM’s transformation into an Enterprise AI Consulting firm organized around three pillars:

Data & AI

• Data & AI foundations, RAG, model portfolio and routing, SLMs & edge, and agentic systems.

Security & AI

• Threat-modeling automation, DevSecOps agents, and secure-by-design implementation patterns.

Observability

• Feedback loops for RL agents, model evaluations, and AI reliability scoring.

Where AVM Is Doubling Down Post–re:Invent 2025

Reinforcement Learning for Agents

RL principles will underpin how we build, evaluate, and improve enterprise agents using telemetry, evaluations, and real business metrics.

SLMs on EKS

Running domain-tuned Small Language Models on enterprise EKS clusters will give organizations more control over latency, cost, data residency, and sovereignty, while still integrating seamlessly with frontier models on Bedrock.