Get to your first working agent in minutes: Announcing new features in Amazon Bedrock AgentCore

2026-04-22 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, short

Summary

AgentCore has introduced new capabilities designed to streamline the development and deployment of AI agents, allowing developers to focus on agent logic rather than infrastructure. The platform now offers a managed agent harness feature, which replaces manual infrastructure setup with a configuration-driven approach, enabling agents to run in three API calls. This harness, powered by the open-source Strands Agents framework from AWS, handles compute, sandboxing, tool connections, persistent storage, and error recovery. Additionally, the new AgentCore CLI provides a unified workflow for building, deploying, and operating agents from a single terminal, supporting infrastructure as code with CDK and upcoming Terraform integration. AgentCore also includes pre-built skills for coding assistants like Kiro and Claude Code, offering curated best practices and accurate context for platform usage. These features are available in preview in four AWS Regions: US West (Oregon), US East (N. Virginia), Asia Pacific (Sydney), and Europe (Frankfurt), with coding agent skills launching by the end of April.

Key takeaway

For AI Architects and VP of Engineering overseeing agent development, AgentCore's new managed harness and CLI significantly reduce infrastructure overhead. Your teams can validate agent ideas in minutes instead of days by focusing on agent logic and configuration changes. This accelerates prototyping and ensures a consistent, reproducible deployment pipeline from development to production, minimizing re-architecture efforts as agents evolve.

Key insights

AgentCore simplifies AI agent development by abstracting infrastructure, enabling rapid prototyping and seamless production deployment.

Principles

• Configuration over code for rapid iteration
• Unified workflow across agent lifecycle
• Infrastructure as code for reproducibility

Method

Define agent parameters (model, tools, instructions) via API calls; AgentCore's harness provisions compute, tooling, memory, identity, and security for execution.

In practice

• Test agent variations by changing API parameters
• Use AgentCore CLI for local iteration and production deployment
• Integrate pre-built skills into coding assistants

Topics

• Amazon Bedrock AgentCore
• Managed Agent Harness
• AI Agent Development
• AgentCore CLI
• Infrastructure as Code

Code references

• aws/agentcore-cli

Best for: AI Architect, CTO, VP of Engineering/Data, AI Engineer, Machine Learning Engineer, MLOps Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.