> Markdown version of [/videos/100298-one-interface-two-audiences-designing-clis-for-humans-and-ai-agents?t=1394](https://www.wearedevelopers.com/videos/100298-one-interface-two-audiences-designing-clis-for-humans-and-ai-agents?t=1394). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

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# One Interface, Two Audiences: Designing CLIs for Humans and AI Agents

Interactive prompts halt autonomous agents in their tracks. Learn how to engineer zero-config, agent-ready CLIs that seamlessly serve developers, LLMs, and graphical interfaces from a single foundation.

- **Speakers:** [Gala Dvoretskaya](https://www.wearedevelopers.com/@gala-dvoretskaya), [Sean O'Dell](https://www.wearedevelopers.com/@sean-o-dell)
- **Event:** World Congress 2026 Europe
- **Published:** July 10, 2026
- **Duration:** 27:08
- **URL:** https://www.wearedevelopers.com/videos/100298-one-interface-two-audiences-designing-clis-for-humans-and-ai-agents

## Summary

As agentic engineering becomes a reality, traditional command-line interactions often create friction when AI agents operate without a human in the loop. The speakers from Dynatrace address this challenge by presenting two revamped CLIs—`dtwiz` for system instrumentation and `dtctl` for platform resource management. Built from the ground up, these tools are designed to work equally well for developers typing in a terminal and AI agents executing autonomous commands, ensuring seamless operability without maintaining separate documentation.

Bridging the gap between human flexibility and machine precision requires deliberate interface design choices. Relying on zero-configuration defaults minimizes initial friction for both audiences, while implementing non-destructive dry-run capabilities allows users and AI to confidently validate changes before execution. Because interactive confirmations (like blocking yes/no prompts) halt autonomous loops, the tools eliminate them in favor of command flags, simultaneously leveraging structured output modes to supply precise, predictable context that maximizes token efficiency for LLMs.

Delivering a holistic experience means recognizing that human developers still benefit from visual validation against agent-driven actions. The session demonstrates how the underlying CLI logic seamlessly powers an intuitive GUI onboarding flow, using auto-discovery to map services and instantly generate corresponding dashboards. Ultimately, the unified architectural blueprint proves that development teams do not need to choose between building a graphical interface, a CLI, or an agent skill—a shared structural foundation effectively supports all three.

**Keywords:** agentic engineering, CLI design, zero-configuration defaults, dry-run validation testing, structured output formatting, autonomous AI agents, dtctl platform management, dtwiz local instrumentation, open telemetry collection, observability platform onboarding, human-in-the-loop workflows, prompt blocking avoidance, LLM token efficiency, automated system discovery, unified interface logic, GUI vs CLI tradeoffs

## Chapters

1. **Designing observability interfaces for human and agent interaction** (01:46) — Adapting command line experiences allows both manual terminal operations and autonomous tools to analyze telemetry data without interactive blocking.
1. **Core design patterns for agent-friendly command line tools** (05:37) — Implementing zero-configuration defaults, structured outputs, and functional dry runs prevents pipeline-blocking interactive prompts.
1. **Automating telemetry ingestion using specialized installation wizards** (08:23) — A unified terminal utility simplifies the slow process of configuring collector endpoints across distributed environments.
1. **Auto-discovering and instrumenting local backend microservices** (10:49) — Running an auto-discovery command scans local systems to identify and instrument running application projects without manual configuration.
1. **Visualizing distributed application topology and trace logs** (15:04) — The graphical interface maps relationships between connected services to surface failed requests and trace execution paths.
1. **Executing platform management commands using artificial intelligence agents** (17:30) — Delegating dashboard creation via natural language highlights how autonomous tools process codebase context prior to deploying resource updates.
1. **Generating structured output formats for autonomous data evaluation** (21:01) — Appending an agent-specific execution flag produces machine-readable semantic structures identical in intent to human-facing terminal results.
1. **Balancing human interaction needs with autonomous machine interfaces** (23:14) — Providing both visual dashboard tools and deterministic terminal commands creates an operational ecosystem robust enough for diverse engagement styles.

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- [The convergence of user and developer experience](https://www.wearedevelopers.com/videos/100278-designing-for-agents-will-make-you-better-at-designing-for-humans) (from "Designing for Agents Will Make You Better at Designing for Humans")
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