The Automaton Foundry

[!NOTE] Status: Content ready · Media: Generated cover and original SVG illustration · Last verified: 2026-09-05
Primary capability: Building an application with the GitHub Copilot SDK

[!TIP] Download this learner kit and use the extraction and setup guide. Keep the original starter untouched.

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sequenceDiagram
    accTitle: The Automaton Foundry capability map
    accDescr: Source structure and evaluation plans are prerequisites, not proof of a live model result. Record errors and cleanup observations explicitly.
    participant U as Caller
    participant A as Application
    participant S as SDK session
    U->>A: Prompt input
    A->>S: Create SDK session
    A->>S: sendAndWait
    S-->>A: Content or explicit failure
    A-->>U: Observed result
    A->>S: Cleanup in lifecycle boundary

Legend. The application owns lifecycle and identity. The caller is not the same actor as the SDK runtime.

Explanation. Source structure and evaluation plans are prerequisites, not proof of a live model result. Record errors and cleanup observations explicitly.

Official references

These official sources are the authority for product behavior and availability. Recheck them when using a different surface or after a product update.

• GitHub Copilot SDK
• Get started with the Copilot SDK
• Copilot SDK features

Story

The Foundry shapes automatons from models, instructions, tools, identity, and evaluations. A polished shell is not evidence of quality.

The fantasy is a memory aid; the engineering lesson requires observable, reproducible evidence.

Learning objectives

• Build the pinned SDK application while keeping development and runtime agents distinct.
• Handle prompt input, session output and client cleanup without success-shaped failure output.
• Separate five declared evaluation cases from actual authenticated model observations.

Prerequisites

For tools and personal accounts, complete the prerequisites guide. For terminal-only study, follow the extracted-kit CLI route; VS Code-specific evidence remains separate.

Evidence boundary: The structural verifier does not measure model quality or authenticate the application. Follow the professional SDK lab for stronger offline tool and lifecycle tests.

Estimated session: 45–75 minutes after prerequisites; actual duration varies. Never use production secrets or customer data.

Concept explanation

The GitHub Copilot SDK embeds the Copilot agent runtime in an application. The runtime agent is different from the GitHub Copilot role used to author it. Sessions, tools, permissions, authentication, custom agents, hooks, MCP, and observability are explicit application concerns. SDK feature availability varies by language and environment; MCP integration is documented as evolving, so verify the current feature page before depending on it.

Concrete use case

An application can send a prompt and print a response yet still fail on timeout or unsupported requests. Its evaluation plan must ask what evidence distinguishes these paths.

Vocabulary checkpoint

• Role: Ask, Plan, Agent, or a custom agent.
• Harness/surface: the runtime or product surface in which a role operates.
• Target: the selected session destination, such as Local, Copilot, or Cloud where exposed.
• Environment: the folder, worktree, local machine, Codespace, or remote environment.
• Instructions: automatically applied durable context.
• Prompt: a manually invoked task template.
• Skill: reusable expertise loaded when relevant.
• Custom agent: a role with instructions, tools, and optional handoffs.
• MCP: Model Context Protocol.
• Evidence: a path, diff, command result, trace, or review decision.

Ask → Plan → Agent workflow

Ask — investigate

1. Identify the real user outcome and current source of truth.
2. Inspect relevant files, instructions, tools, permissions, and existing checks.
3. Cite concrete paths or platform evidence for every important finding.
4. Record uncertainty and do not infer unavailable capabilities.

Gate: No implementation until the current state and evidence are understood.

Plan — design

1. State scope, non-goals, assumptions, risks, and trust boundaries.
2. Select the minimum role, tools, authority, and environment.
3. Define acceptance criteria, verification commands, review, and reset.
4. Mark any Preview or experimental dependency and provide a fallback.

Gate: Another learner should be able to predict completion from the plan.

Agent — execute

1. Make the smallest reversible change or produce the planned artifact.
2. Use short inspect → change → verify loops.
3. Preserve command output, exit status, diffs, traces, or platform records.
4. Stop when acceptance criteria are met; do not perform unrelated cleanup.

Review — challenge

1. Inspect the complete diff or artifact.
2. Compare each result with the plan and acceptance criteria.
3. Run the narrowest relevant existing check, broadening only when justified.
4. Record limitations and unresolved risks.
5. Score the work with rubric.md.

Guided mission

1. Prepare one isolated copy

1. Download and extract the automaton-foundry kit into a new work-drive directory.
2. Read KIT-START.md at its root. Open starter/ as the VS Code workspace when testing discovery, but run the verifier from the kit root.
3. Inspect starter/index.ts, starter/package.json, starter/evaluation.json, verify.js before editing.
4. From the extracted kit root, run node verify.js.
5. Record the documented starter rejection. A missing runtime or unrelated crash is not the expected exercise result.

2. Investigate and plan

Copyable baseline command, from the extracted kit root:

node verify.js

In Ask, request a trace of the inspected files and what the verifier actually observes. Challenge any claim about live execution that is not supported by output.

Use this planning prompt:

Complete the pinned TypeScript SDK lifecycle and CLI prompt input. Keep credentials out of source, stop the client in finally, and define five cases with expected evidence before any optional live request.
Do not implement yet. Identify affected files, the negative case and a safe reset.

3. Implement the reviewed slice

1. Approve only the named starter artifact and necessary focused tests.
2. Ask Agent to implement one slice; inspect proposed commands before execution.
3. Run node verify.js again from the kit root, or node ../verify.js from starter/.
4. Compare the exact result with the checkpoint below and review the complete diff.
5. Record host discovery or live activity separately when available. Do not enable extra services to manufacture a passing result.

[!IMPORTANT] Checkpoint: The structural verifier accepts the source and five declared cases; live results, if any, are recorded separately. The structural verifier does not measure model quality or authenticate the application. Follow the professional SDK lab for stronger offline tool and lifecycle tests.

4. Prove a check can reject a mistake

Remove the client cleanup from the copied application and confirm structural rejection. Restore it; do not infer runtime cleanup merely from this static test.

Finish with the adventure-specific capability evidence in the rubric, not just the presence of a file.

Intentional failure: The Unmeasured Automaton

Perform this only in a disposable environment:

Declare the agent accurate after one successful example. A demonstration is not an evaluation because dataset, criteria, variability, and failures are uncontrolled.

Recovery

Return to Ask, identify the violated boundary, narrow the plan, remove unnecessary authority or context, and repeat the smallest relevant verification. Document the causal lesson rather than merely stating that the attempt failed.

Independent challenge

Design a one-tool agent with identity, authorization, timeout, retry, audit, and evaluation behavior.

Constraints:

• Do not copy the guided mission verbatim.
• Do not add tools, permissions, or cloud resources without a stated need.
• Do not claim quality, performance, compatibility, or availability without executed evidence.
• Keep fantasy language subordinate to technical clarity.

Evidence checklist

• Source-grounded Ask findings.
• Approved plan with scope, non-goals, risks, checks, and reset.
• Agent diff or artifact limited to the declared scope.
• Verification output with command, status, or platform record.
• Intentional-failure root cause and recovery.
• Independent challenge result.
• Explicit limitations and feature-status notes.
• Completed rubric.md.

Reset instructions

1. Save your diff, command output and limitations from the disposable kit.
2. Stop only the process or learning session you started. Do not stop other projects.
3. If you initialized Git in the kit, inspect git status --short there and restore only your named exercise files from its local baseline.
4. Otherwise, extract the original ZIP into a new unused directory for another attempt; do not overwrite your current work.
5. Remove only exercise-owned configurations, worktrees or remote resources after reviewing anything worth preserving.

The curriculum source and other projects must remain unchanged. A reset of the copied kit is not a repository-wide hard reset.

Next adventure

The Convergence of Three Realms