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# AX is the only Experience that Matters

Is your SaaS ready for autonomous AI? Agents are replacing human developers as the primary users of software tools, making Agent Experience (AX) the only metric that matters.

- **Speakers:** [Ivan Burazin](https://www.wearedevelopers.com/@ivan-burazin)
- **Event:** World Congress 2025
- **Published:** August 20, 2025
- **Duration:** 25:14
- **URL:** https://www.wearedevelopers.com/videos/1404-ax-is-the-only-experience-that-matters

## Summary

The software landscape is experiencing a paradigm shift where autonomous AI agents—not human developers—will soon be the primary consumers of developer tools. However, modern SaaS infrastructure often breaks the moment human-in-the-loop requirements like manual authentication, GUI navigation, or debugging are removed. To build for the future, companies must prioritize "Agent Experience" (AX): enabling AI to autonomously access, understand, and operate within digital environments to achieve user-defined goals without human intervention.

Optimizing for AX requires replacing human interfaces with machine-native solutions. Pioneering examples include seamless authorization layers (like Arcade) that manage secure logins on behalf of agents, and agent-readable documentation (such as Stripe's direct Markdown rendering or the emerging `llms.txt` standard) that preserves token limits and prevents context window exhaustion. Ultimately, every feature must be accessible via an API rather than graphical dashboards.

To execute tasks natively, agents also demand completely reimagined infrastructure known as "agent-native runtimes." Unlike traditional computing environments, these digital sandboxes must boot with near-zero latency (under 90ms) while remaining stateful and long-running. Advanced frameworks like Daytona are pushing these boundaries by equipping agents with headless terminals, declarative Docker image builders, persistent shared volumes for large datasets, and the ability to fork environments for parallel execution testing. This unprecedented market pivot proves a stark reality for modern software: "If agents can't use your product in the future, probably nobody will."

**Keywords:** agent experience, autonomous ai agents, machine-native interfaces, agent authorization layers, agent-readable documentation, llms.txt standard, api-first development, agent-native runtimes, stateful serverless architecture, headless terminal execution, declarative image builders, parallel agent execution, container volume sharing, autonomous developer tools

## Chapters

1. **The exponential rise of autonomous software agents** (00:04) — The growing number of software agents outnumbering humans requires a fundamental shift beyond traditional deterministic web applications.
1. **Defining the core components of an ai agent** (02:32) — Autonomous software functions strictly at the intersection of intelligence models, user prompts, and accessible digital tooling.
1. **Understanding the core principles of agent experience** (04:14) — Simplifying digital environments enables machines to easily access, comprehend, and operate within systems to achieve user-defined goals.
1. **Exploring real-world examples of seamless agent tooling** (05:36) — Implementing authentication layers and machine-readable text files improves the ability of agents to navigate complex api documentation securely.
1. **Designing software systems for complete agent autonomy** (08:34) — Removing fallback requirements ensures tools remain functional even when no human is available to troubleshoot errors or click buttons.
1. **Building fast and stateful agent native runtimes** (11:28) — Creating custom bare-metal sandboxes solves the unique requirement for agent environments that spin up instantly but run stateful tasks indefinitely.
1. **Implementing machine native interfaces for autonomous tasks** (14:41) — Replacing human user interface components with headless terminals and language servers allows optimal machine-to-machine communication.
1. **Developing new infrastructure primitives for automated workflows** (16:35) — Equipping agents with declarative image builders and shared network volumes removes human bottlenecks in container orchestration and dependency management.
1. **Leveraging parallel execution for automated problem solving** (19:35) — Forking system runtimes drastically multiplies computational capabilities by enabling an agent to test varying programmatic solutions simultaneously.
1. **Meeting the market demand for agent computing infrastructure** (21:09) — Iterating with developers reveals significant gaps in current serverless offerings and highlights an explosive market demand for machine-first hosting platforms.

## Related Moments

- [Defining true agent behavior versus traditional enterprise chatbots](https://www.wearedevelopers.com/videos/1538-composable-intelligence-how-henkel-and-microsoft-are-shaping-the-agent-ecosystem) (from "Composable Intelligence: How Henkel and Microsoft Are Shaping the Agent Ecosystem")
- [Transitioning software engineering teams to AI-native development workflows](https://www.wearedevelopers.com/videos/100087-ai-ready-what-enterprise-transformation-actually-takes) (from "AI-Ready? What Enterprise Transformation Actually Takes")
- [Balancing developer autonomy with the adoption of coding agents](https://www.wearedevelopers.com/videos/100198-the-last-mile-of-ai-from-prototype-to-production) (from "The Last Mile of AI: From Prototype to Production")
- [Fusing developer experience and platform engineering for agentic SDLC](https://www.wearedevelopers.com/videos/100266-ai-won-t-fix-your-engineering-culture) (from "AI Won't Fix Your Engineering Culture")
- [Exploring AI agent usage within the software engineering industry](https://www.wearedevelopers.com/videos/1814-wearedevelopers-live-markdown-liquid-and-checkouts) (from "WeAreDevelopers LIVE - Markdown, Liquid and Checkouts")
- [Rethinking business processes for autonomous enterprise agents](https://www.wearedevelopers.com/videos/100124-when-ai-runs-the-business-the-reality-of-enterprise-wide-automation) (from "When AI Runs the Business: The Reality of Enterprise-Wide Automation")

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