Agent-Native Interfaces

The 2024–2026 dimension of client-agnostic core thinking: designing software interfaces specifically so AI agents (LLMs invoking tools, MCP servers, Claude Code, Cursor agents) can drive them effectively. Not by retrofitting an "AI mode" onto existing UIs, but by treating agents as a first-class consumer of the API/CLI from day one.

The core insight: good design for agents is also good design for shell pipelines and other programs. Agents are simply the most demanding consumer of the contract Doug McIlroy described in 1978 ("write programs to handle text streams").

What makes an interface agent-native

Six properties accumulated from ~2024–2026 practice:

1. Structured outputs always available

JSON output is a first-class flag, not an afterthought. --format json (or auto-detected from non-TTY stdout). Stable schemas. Breaking the schema breaks downstream agents silently.

Example: gh (GitHub CLI) ships JSON output for almost everything. aws does too. kubectl get -o json is universal. mxr and lazydap inherit this.

2. Idempotency and dry-run as first-class

Agents will retry. They'll re-issue the same command after a network blip. Mutating endpoints need idempotency keys (Stripe-style) or --dry-run previews so the agent can verify intent.

Both Mxr and Lazydap enforce: every mutation has --dry-run. Selection logic in dry-run matches the real mutation. Same code, same outcome.

3. Predictable schemas

Type definitions are the contract. Optional fields, error shapes, nested structures — all documented. Schema files (JSON Schema, OpenAPI, gRPC .proto) help agents validate inputs before sending.

Modern best practice: ship schemas alongside the API. MCP servers expose tools/list returning each tool's input schema. CLI tools should expose --schema or similar.

4. Semantic identifiers, not opaque pointers

Agents reason about entities by their identifiers. customer_acct_01ABC (semantic) vs f3xz9q (opaque) — the first lets an agent understand "this is a customer account"; the second forces every reference to be looked up.

Rule of thumb: prefix IDs with their type. UUIDv7 plus a type prefix is a popular shape. msg_01HXYZ, bp_01ABC (from lazydap's breakpoint IDs).

5. Capability discovery

Agents need to ask "what can I do here?" without reading documentation. APIs that don't expose their own surface programmatically force agents to be hand-tuned per integration.

Mechanisms:

• LSP / DAP: initialize returns a Capabilities object listing supported methods.
• MCP: tools/list returns every tool's name + description + schema.
• CLIs: --help is human-friendly but parseable; some tools add a skills or manifest subcommand returning structured discovery.

CLIWatch's "Designing a CLI Skills Protocol for AI Agents" (2025) argues for a standard skills subcommand. Worth watching.

6. Self-contained, non-overlapping tool surface

Two tools that do nearly the same thing confuse LLMs at tool selection. A clean surface has each operation in exactly one place. Anthropic's Writing tools for agents emphasises this:

"Every tool must justify its existence."

Reduce surface area; consolidate similar operations; document each tool's distinct purpose.

Design rules from Anthropic's "Writing tools for agents"

Anthropic's engineering post is the closest thing to a canonical guide for this:

• Meaningful namespacing. github_list_prs over list. Names that include the system + the action.
• Stable semantic IDs. Don't expose internal database row IDs.
• High-signal, low-token responses. Don't return paginated nothing; return what the agent actually needs.
• Concrete input examples in descriptions. Few-shot prompting baked into the tool schema.
• Error responses that suggest fixes. Not just "invalid input" but "expected ISO 8601 date, got '2026-05-01 12:00'."

These rules apply equally to JSON CLIs, REST APIs, and MCP tools.

Two emerging "best practice" articles worth reading

• InfoQ — Keep the Terminal Relevant: Patterns for AI Agent Driven CLIs: every command needs a machine-friendly escape hatch (JSON output flag, env vars, semantic exit codes). Treat structured outputs as stable API contracts.
• CLIWatch — Designing a CLI Skills Protocol for AI Agents: --help is for humans; agents need a skills discovery mechanism returning structured JSON.

Both crystallise patterns that mxr and lazydap arrived at independently.

Why this is newly important

Pre-LLM, "agent-friendly CLI" was a niche concern (cron jobs, CI scripts). Post-LLM, every popular CLI is being driven by agents in some capacity. Tools that don't ship structured output, idempotency, dry-run, and discovery are increasingly invisible to LLM-based development.

This isn't a marketing observation. It's a structural one: agents are doing real work in 2026, and the tools they reach for are the ones that are easy to reach for.

Simon Willison's framing

Simon Willison is the most-cited voice on agent-CLI design. Two posts to read:

• "Designing agentic loops" (Sept 2025) — "carefully selecting tools to run in a loop to achieve a specified goal."
• "Large Language Models can run tools in your terminal with LLM 0.26" (May 2025) — his llm CLI as the canonical "shell-as-agent-substrate" tool.

Willison explicitly prefers shell commands over MCP for coding agents: "coding agents are really good at running shell commands… create an AGENTS.md with examples." This is exactly the Mxr / Lazydap design intuition, articulated.

Native vs retrofitted

Two approaches to "AI-friendliness":

1. Retrofit. Existing tool, add an "AI mode" or MCP wrapper later. Hard; the original surface wasn't designed for this.
2. Native. Design the tool such that the AI usage is the same as the human usage (with maybe a --format json flag). Easier; the discipline that makes humans' lives easier (composable, scriptable, predictable) makes agents' lives easier too.

mxr and lazydap take approach 2. The CLI is one consumer. The agent skill is another. Both speak the same protocol. No special "AI mode."

What sufficient agent-nativity looks like

A checklist for new tools:

• --format json (or auto-detect non-TTY) on every output-producing command
• Stable JSON schemas; documented schema location
• --dry-run on every mutation; identical selection logic to real mutation
• --yes to skip confirmation prompts
• Semantic exit codes (0/1/2/3 distinguishable)
• Errors as structured JSON with codes, not just text
• Per-subcommand --help; ideally also a --schema or manifest for programmatic discovery
• AGENTS.md (or .skill ZIP) with examples
• No required interactive prompts in non-TTY mode
• Idempotent reads; idempotency keys or natural deduplication on writes

Hit 8 of 10 and your tool is in the top decile of agent-friendliness. Hit all 10 and agents will reach for it before alternatives.

See also

• Client-Agnostic Cores — the broader synthesis
• API-First Design — the methodology
• Headless Architecture — the architecture
• LSP and the X-Protocol Family — the protocol pattern
• Mxr · Lazydap — concrete examples
• Anthropic's "Writing tools for agents": https://www.anthropic.com/engineering/writing-tools-for-agents
• Simon Willison on agentic loops: https://simonwillison.net/2025/Sep/30/designing-agentic-loops/
• CLI Guidelines (clig.dev): https://clig.dev/