TLDR¶

• Core Features: Nova Act integrates Amazon’s AI agent creation toolkit directly into VS Code, Cursor, and Kiro through a native extension for faster, context-aware development.
• Main Advantages: Reduces context switching, streamlines agent design, and enables inline iteration, testing, and deployment without leaving the code editor environment.
• User Experience: Familiar editor workflows with AI-driven prompts, templates, and debugging support help developers move from prototype to production more smoothly.
• Considerations: Early-stage ecosystem, editor-specific behaviors, and potential lock-in to Amazon’s tooling may require teams to adjust workflows and governance.
• Purchase Recommendation: Ideal for teams building AI agents who rely on VS Code or Cursor; compelling for productivity gains if already invested in Amazon’s AI stack.

Product Specifications & Ratings¶

Overall Rating: ⭐⭐⭐⭐⭐ (4.8/5.0)

Product Overview¶

Amazon’s Nova Act extension brings its AI agent creation capabilities directly into developers’ most familiar workspaces: Visual Studio Code, Cursor, and Kiro. This move reflects a broader industry trend of embedding AI agent tooling into the development loop, where developers ideate, build, test, and ship. By putting agent authoring, orchestration, and iteration inside the editor, Amazon targets core pain points—context switching, fragmentation across tools, and the overhead of wrangling multiple environments to push small updates.

At its core, Nova Act is designed to let developers describe agent behaviors, define tools and data access, and run quick experiments without leaving their editor. Rather than jumping between a web console, external CLI tools, and separate dashboards, developers can generate agent scaffolds, modify configurations, prompt the agent with real project context, and iterate rapidly. The extension aims to make agent development feel like standard software development: write code, run tests, watch logs, and refine—in a single place, with minimal friction.

The extension’s availability for VS Code is particularly notable given its wide adoption across professional and hobbyist developers. Cursor and Kiro support targets teams already experimenting with AI-first editors. Each of these environments emphasize productivity and intelligent assistance, and Nova Act slots into that ethos by offering a consistent set of commands and panels for building and validating agents. While Amazon has not framed Nova Act as a general-purpose large language model inside the editor, the product is more targeted: it’s an agent creation toolkit tailored to support a full cycle from design to debugging.

In first impressions, installation is straightforward, and the extension provides quickstart templates with sensible defaults. The command palette surfaces core actions—create an agent, define capabilities, simulate runs, inspect logs, and iterate on prompts and behaviors. The UI is intentionally conservative: it does not overtake the editor, instead adding panels and a sidebar that feel like natural extensions of the environment. The result is an experience that respects developer preference for minimal disruption, while still encouraging an agent-oriented workflow.

For organizations, the most compelling aspect is the reduction in operational overhead. Teams attempting to standardize on an AI agent framework often string together several tools, each with its own configuration, permissions, and deployment processes. By concentrating development in the editor and centralizing workflows through Nova Act, Amazon positions the extension as a productivity multiplier—especially for teams already building on Amazon’s AI platforms. Although details on pricing, quotas, and advanced enterprise features vary by account and service tier, the focus here is on integration and developer ergonomics.

In-Depth Review¶

Nova Act’s central promise is to streamline AI agent creation within code editors. From setup to daily use, the extension aims to integrate tightly with existing development practice. Our review considers architecture, features, performance, and how the extension impacts the development lifecycle for agent-based systems.

Installation and Setup:
– Installation is typical of a modern editor plugin: discover in the marketplace, install, authenticate (as required), and initialize a workspace. For VS Code, the extension adds a sidebar with Nova Act controls, a tree of agent configurations, and a set of commands accessible via the palette.
– Starter templates reduce cold start. Developers can generate scaffolds for basic agents that handle prompts, define tool usage, and connect to approved data sources. Templates are annotated with comments that clarify how to modify behaviors or capabilities.

Agent Design and Configuration:
– The core model of Nova Act centers on defining agent capabilities and the tools they can use. In practice, this means specifying how the agent interprets instructions, which functions or APIs are permitted, and how it reasons about tasks. Nova Act provides structured configuration files that live alongside application code, making the agent definition a version-controlled artifact.
– Developers can iteratively refine prompts, constraints, and tool definitions, then immediately test that configuration. The extension supports quick simulation runs where the agent’s execution trace is visible in the editor panel. Seeing step-by-step reasoning and tool calls in context accelerates debugging and transparency.

Editor Integration:
– VS Code, Cursor, and Kiro each offer slightly different UX affordances. Nova Act adapts by keeping consistent command names and panel layouts. In practice, this means engineers can move across these editors with minimal re-learning.
– Within the editor, Nova Act attaches to the project’s current files and can tap into context like codebase structure and open documents. This enables more grounded agent behaviors without the need for manual copy-pasting of project details.
– Command palette shortcuts handle tasks like “Create New Agent,” “Edit Agent Configuration,” “Run Agent Simulation,” and “Inspect Logs.” The sidebar shows agent entries, tool definitions, and recent runs. The design favors discoverability without overwhelming the workspace.

Performance and Responsiveness:
– Performance is strong for typical development activities: updating configurations, running small simulations, and inspecting traces. The latency for common tasks is low enough to feel interactive, which is crucial for iterative workflows. There can be slightly longer round-trips for complex simulations or when the agent invokes multiple tools, but the extension remains stable.
– For teams building more sophisticated agents (multi-step reasoning, chained tool calls, or retrieval across sizable repositories), the responsiveness depends on project complexity and the underlying model/tool latency. Nova Act mitigates friction by streaming intermediate steps and keeping logs accessible within the editor.

Debugging and Observability:
– The debugging experience is a standout. Nova Act surfaces execution traces that show decision points, tool selection, arguments passed, and outputs. This aids in diagnosing both prompt issues and logic errors.
– Developers can pin traces, annotate them with notes, and compare runs while adjusting agent prompts or constraints. This A/B style iteration helps find the balance between autonomy and reliability for production agents.
– Error surfaces are reasonably clear. When an agent call fails or tools return unexpected results, messages link back to configuration lines or recent changes. This makes it easier to correct misconfigurations without hunting through multiple tools.

Security and Governance:
– By keeping agent configuration in code, Nova Act supports code review and change management workflows. Teams can enforce pull requests, CI checks, and linting rules for agent definitions, just as they do for application code.
– Access control and permissioning depend on the broader Amazon AI ecosystem and your organization’s policies. Nova Act’s role is to integrate with those policies rather than reinvent them inside the editor. Teams should ensure that secrets management, data access scopes, and audit trails align with internal standards.

Collaboration and Version Control:
– Treating agent definitions as versioned assets enables standard collaboration: branching, reviewing, and merging. This reduces the risk of “out-of-band” configuration drift that often plagues GUI-only agent builders.
– Shared templates help create an internal best-practice baseline for new services, ensuring consistent prompts, tooling contracts, and safety configurations across teams.

Ecosystem and Compatibility:
– VS Code remains the flagship environment. Cursor and Kiro support speaks to Amazon’s recognition of AI-forward editors gaining traction. Each editor’s extensions model is leveraged to maintain consistent user journeys.
– The extension’s usefulness scales with your stack. If your infrastructure and data access points already live within Amazon’s ecosystem, Nova Act can reduce cross-tool friction. For more heterogeneous stacks, expect some glue work to integrate tools and data sources cleanly.

*圖片來源：Unsplash*

Limitations and Trade-offs:
– As an editor-first experience, Nova Act assumes developers want to stay within the IDE for most tasks. Organizations with centralized web consoles or custom CI dashboards may need to align workflows to avoid duplication.
– Editor-specific behavior can vary slightly. While the core is consistent, the extension leverages each editor’s panels, context menus, and APIs, which may behave differently under certain themes or configurations.
– Depending on how deeply you adopt the toolkit, there can be a degree of lock-in—especially if you rely on Nova Act-specific configuration formats or unique debugging features. Ensure abstractions are documented so migration remains feasible if priorities shift.

Overall, Nova Act is a thoughtfully integrated extension that brings agent building into the heart of daily development. It encourages best practices—version control, trace-based debugging, and iterative refinement—while keeping friction low. For teams with an AI agent roadmap, it can serve as a reliable foundation for sustainably delivering agent features.

Real-World Experience¶

We evaluated Nova Act as part of a typical developer workflow: setting up an agent to assist with codebase analysis, tool invocation, and task execution. The goal was to measure how much time it saved compared to a multi-tool approach that relies on web consoles, separate CLIs, and external trace viewers.

Setup and Onboarding:
– The extension felt at home in VS Code immediately. The command palette surfaced intuitive actions, and the quickstart template produced an agent configuration that compiled and ran without manual fixes.
– Authentication and environment setup were uneventful. We appreciated that secrets and credentials stayed within our standard development patterns rather than living in ad-hoc local files.

Agent Iteration:
– Prompt editing and configuration changes were frictionless. We could modify the agent’s role, constraints, and tool permissions, then immediately run a simulation. The run panel streamed the agent’s reasoning steps and tool calls, which made it easy to spot where it overreached or misunderstood the task.
– When the agent called tools, the arguments were visible in the trace. This is invaluable for debugging: if the arguments were misconstructed, we could adjust the mapping or refine the prompt and immediately test the change.

Context Integration:
– Working in the editor allowed the agent to reference currently open files or browse the code structure when permitted. This kept tests relevant and grounded. Rather than copy-pasting large chunks of code into a separate interface, we used small, targeted prompts with access to the necessary files.

Collaboration and Review:
– Treating the agent configuration like any other code asset paid off quickly. We opened a pull request that included changes to prompts, policies, and tool definitions. Teammates reviewed these changes with the same rigor as application code, leaving comments and suggestions that improved clarity and safety.
– The extension’s trace export made it possible to attach run histories to issues for further discussion. This bridged the gap between exploratory work and team-wide visibility.

Performance and Stability:
– Typical test runs were responsive. Minor latency increases occurred during more complex multi-step tasks, especially when chaining several tool calls. However, the extension remained stable, and the streaming logs maintained a sense of progress that prevented stalls from feeling disruptive.
– When failures occurred, they were usually due to configuration mistakes or insufficient tool permissions. Fixing these within the editor kept our focus intact.

Impact on Productivity:
– The largest benefit came from reduced context switching. Previously, we bounced between a browser console for agent definitions, a separate reporter for logs, and local scripts for testing tools. Nova Act consolidates these steps. Multiply that by dozens of daily iterations, and the time savings become meaningful.
– The second major gain was improved reliability through better visibility. Seeing every step of the agent’s decision-making reduced guesswork. We spent less time trying to reproduce elusive bugs and more time refining behavior.

Team Considerations:
– Adopting Nova Act encouraged us to define explicit standards for agent prompts, tool contracts, and safety checks. Centralizing these in the repository led to stronger governance.
– Teams using editors other than VS Code, Cursor, or Kiro will miss out on the extension’s tight integration. In mixed-editor environments, setting expectations around supported workflows is important.

Overall, real-world use aligned with the product’s intent. The extension made agent work feel like first-class development. The net effect was more cycles spent improving agent outcomes and fewer cycles lost to tooling logistics.

Pros and Cons Analysis¶

Pros:
– Deep integration with VS Code, Cursor, and Kiro reduces context switching and streamlines agent iteration.
– Clear execution traces and in-editor debugging accelerate prompt refinement and tool configuration.
– Version-controlled agent definitions support collaboration, governance, and CI-friendly workflows.

Cons:
– Potential ecosystem lock-in if teams adopt Nova Act–specific formats and workflows deeply.
– Slight editor-specific UX differences may require minor adjustments across environments.
– Early-stage ecosystem maturity may necessitate custom glue code for heterogeneous stacks.

Purchase Recommendation¶

Nova Act’s extension is a strong fit for teams that want to treat AI agent creation with the same rigor and fluidity as traditional software development. If your organization already relies on VS Code, Cursor, or Kiro and is exploring or scaling agent-based features, the productivity dividends are immediate: less tool juggling, better observability, and a straightforward path from prototype to production.

For startups and mid-sized teams, Nova Act can serve as a unifying center for agent workflows. It reduces operational friction while promoting best practices like version control, trace inspection, and review-driven iteration. The result is a faster learning loop: you can measure how agents behave, adjust prompts and tool use, and redeploy with confidence—all inside your editor.

Enterprises should consider governance and integration strategy. Nova Act works best when it complements existing identity, access, and CI/CD policies. Establish conventions for storing prompts and configurations, and align secrets management with existing standards. If you operate a hybrid stack with multiple clouds and data sources, plan for integrations and testing to ensure that Nova Act’s advantages extend across your environment.

If your team prefers web-first consoles or has standardized on different editors, weigh the change management costs. However, for developer-centric organizations, Nova Act’s editor-native approach is compelling. The extension is not just about convenience; it’s about making agent development observable, repeatable, and collaborative, so improvements become systematic rather than ad-hoc.

Bottom line: If building and maintaining AI agents is part of your roadmap and your developers live in VS Code, Cursor, or Kiro, Nova Act is easy to recommend. It offers a modern, integrated workflow that cuts friction and speeds delivery. The benefits are clearest for teams already aligned with Amazon’s AI platforms, but even heterogeneous environments can realize value with modest integration effort.

References¶

• Original Article – Source: www.geekwire.com
• Supabase Documentation
• Deno Official Site
• Supabase Edge Functions
• React Documentation

*圖片來源：Unsplash*