Agentic AI

AWS has generally available runtime instances for Amazon Bedrock AgentCore, enabling agents to run directly on EC2 infrastructure. This option complements existing microVM deployments by supporting GPU, memory, and compute-optimized families for specialized workloads. AgentCore manages provisioning, patching, scaling, and lifecycle, allowing teams to define capacity providers via console or API.

• Run AgentCore on your own EC2 instances without managing underlying infrastructure
• Access dedicated GPU, memory, and compute-optimized instance types for heavy workloads
• Define capacity providers via console, CLI, SDKs, or APIs to specify instance needs
• AgentCore handles provisioning, patching, scaling, and lifecycle management automatically
• Complements existing microVM-based runtime for diverse agent deployment requirements

PostgreSQL has become the default storage for agentic AI, yet many teams treat it merely as a passive parking lot for signals and state. As workloads grow more agentic, databases must actively handle intermediate results, shared state updates, and concurrent multi-step workflows. AI engineers often overlook PostgreSQL's capabilities as a first-class compute citizen, leading to suboptimal handling of complex production patterns.

• Shift mindset from storage-only to active compute layer for AI agents.
• Expect concurrent multi-step workflows and shared state updates.
• AI engineers must explore Postgres compute features beyond CRUD.
• Agentic workloads create unique patterns distinct from traditional ops.
• Optimize for signal, memory, and checkpoint throughput, not just queries.

Amazon Bedrock AgentCore introduces temporal policies to enforce stateful authorization rules based on an agent's session history, ensuring that tool calls are evaluated in context rather than isolation. Additionally, the service now supports rate limiting for AI traffic, allowing administrators to control request volumes per user or group. These controls apply to tools, models, and agents connected via the gateway, scoped by OAuth or AWS IAM permissions.

• Enforce workflow sequencing by requiring tool arguments to match prior outputs.
• Prevent misuse by evaluating tool safety based on the full session context.
• Require human approval before executing privileged actions within an agent.
• Apply per-user or per-group rate limits to tools, models, and agents.
• Scope controls using OAuth or AWS IAM for granular access management.

This paper introduces Agentic Nesting, a methodology designed to overcome the rigidity and high maintenance costs of traditional enterprise integration tools like ESBs and RPA. By leveraging AI agents, the approach aims to seamlessly orchestrate heterogeneous business systems and break down data silos without requiring heavy architectural coupling. The proposed framework seeks to enhance operational intelligence while reducing the complexity of managing diverse information applications.

• Moves beyond rigid middleware architectures to solve enterprise integration challenges.
• Reduces coupling and maintenance costs associated with legacy systems.
• Enables intelligent orchestration of heterogeneous business applications.
• Addresses data silos through dynamic agent-based workflows.