How We Set Up GitHub Copilot Agent Modes for a Specialized Dev Team in VS Code

GitHub Copilot's agent mode in VS Code is one of the most underused productivity tools in the current engineering landscape. Most teams use it for ad-hoc code generation. A smaller number use it as a general-purpose coding assistant. Almost nobody has set it up as a structured, role-specialized development team with enforced delegation rules, workflow-driven behavior, and a testable compliance model.

We have. This is the full technical tutorial — real folder structure, real instruction files, real workflow diagrams — based on the production multi-agent system we run at ODSEA.

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1. What Are GitHub Copilot Agent Modes? A Primer for CTOs

Before diving into configuration files, it's worth establishing what we're actually talking about. GitHub Copilot in VS Code supports custom agent modes through .agent.md files in your .github/agents/ directory. Each file defines a named agent with a specific model, a specific tool set, a specific list of sub-agents it can delegate to, and a body of instructions that governs its behavior.

When a developer opens the GitHub Copilot Chat panel and selects "@TechLead" from the agent dropdown, they're not just changing the system prompt. They're activating a configured agent that has a different model, different tools, different constraints, and different behavioral rules from the default Copilot assistant — or from the "@Developer" agent sitting next to it in the same dropdown.

The model matters. We run our orchestrator agents (ProductManager, TechLead, ResearchLead) on Claude Opus 4.6 — high reasoning capacity, higher cost, used for decisions and architecture. We run our implementation agents (Developer, DatabaseDev, AutomationTester) on Claude Sonnet 4.6 — lower latency, lower cost, appropriate for focused execution tasks. This tier separation is declared directly in the YAML frontmatter of each agent file.

The tool set matters. Our TechLead has GitHub PR tools, issue tools, branch tools. Our Developer has Supabase read-only SQL tools but no GitHub PR creation tools — it can only create the PR through TechLead. Our SecurityReviewer has investigative tools but no write tools of any kind. These constraints are not cosmetic. They prevent agents from taking actions their role doesn't authorize.

The delegation model matters most of all. Every agent has an explicit agents: field listing which sub-agents it's permitted to invoke. An agent not on the list cannot be called. This creates a bounded hierarchy rather than an open-ended chain where any agent can delegate to anything.

This is the architecture we're going to walk through. By the end of this tutorial, you'll have the exact structure to replicate it.

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2. Folder Structure: The Anatomy of a Multi-Agent Workspace

The entire system lives in .github/. Here's the actual structure from our production repository:

.github/
  copilot-instructions.md          ← global controller (all agents inherit this)
  agents/
    product-manager.agent.md
    tech-lead.agent.md
    senior-tech-lead.agent.md
    developer.agent.md
    database-dev.agent.md
    doc-writer.agent.md
    test-lead.agent.md
    tester.agent.md
    automation-tester.agent.md
    api-tester.agent.md
    security-reviewer.agent.md
    researcher.agent.md
    research-lead.agent.md
    designer.agent.md
    web-performance-optimizer.agent.md
    tech-lead/
      git-workflow.md              ← loaded just-in-time by TechLead
      parallel-development.md
      feature-completion.md
    developer/
      coding-standards.md          ← loaded just-in-time by Developer
      api-patterns.md
    shared/
      git-workflow.md              ← shared across agents
      github-issues-workflow.md
  workflows/
    diagrams/
      product-manager-workflow.md  ← primary guide for ProductManager
      techlead-workflow.md         ← primary guide for TechLead
      developer-workflow.md        ← primary guide for Developer
      database-dev-workflow.md
      doc-writer-workflow.md
      test-lead-workflow.md
      tester-workflow.md
      researcher-workflow.md
      security-reviewer (inline in tech-lead workflow)
  skills/
    shared/
      delegation-protocol/
        SKILL.md                   ← context: fork — enforces delegation hard-stop
      agent-task-loop/
        SKILL.md                   ← context: fork — communication loop pattern
      subagent-runtime-contract/
        SKILL.md                   ← context: fork — logging and state contract
      skill-contract-standard/
        SKILL.md                   ← the meta-skill: how to write skills
      workflow-gitignore-safety/
        SKILL.md
      github-issues/
        SKILL.md
    copilot/
      github-workflow/
        SKILL.md
  docs/
    architecture/
      agent-hierarchy.md
      orchestration-patterns.md
      delegation/
        techlead-delegation.md
        developer-delegation.md
        product-manager-delegation.md

A few structural observations worth noting:

Agent files are flat at the top level of .github/agents/ — one .agent.md file per role. Role-specific reference documents live in a subfolder named after the agent. This keeps the agent list scannable while allowing each role to have arbitrarily deep supporting documentation.

Workflow diagrams are separate from agent instruction files. The agent file tells the agent who it is and what it may do. The workflow diagram tells the agent how to do it, step by step, for any given task type. This separation is intentional — instruction files change rarely (role definitions are stable), while workflow diagrams change frequently as procedures improve.

Skills live in .github/skills/ as standalone SKILL.md files, one per skill directory. They're invoked by reference in agent instruction files using a <!-- BOILERPLATE_REFERENCE: ... --> comment that tells the agent to read that SKILL.md at the point of invocation.

copilot-instructions.md is the global controller. It is not an agent instruction file — it applies to every Copilot session regardless of which agent mode is active. This is where global constraints live: delegation architecture, tech stack rules, deployment environment mappings, GitHub issue hierarchy rules. Every agent inherits all of it.

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3. Writing an Agent Instruction File: YAML Frontmatter and Body

Let's look at two real agent files to understand the format.

TechLead — the orchestrator

---
name: TechLead
model: ['Claude Opus 4.6 (copilot)', 'Claude Opus 4.6 (copilot)']
description: Strategic architectural oversight, security compliance, and maintaining code consistency standards.
tools: [
  vscode, execute, read, agent, edit, search, memory, todo, web,
  vscode_askQuestions,
  github/issue_read, github/issue_write, github/list_issues,
  github/search_issues, github/add_issue_comment,
  github/sub_issue_write, github/pull_request_read,
  github/pull_request_review_write, github/list_pull_requests,
  github/search_pull_requests, github/create_pull_request,
  github/merge_pull_request, github/update_pull_request,
  github/update_pull_request_branch, github/create_branch,
  github/list_commits, github/get_commit, github/get_me,
  github/web_search, github/search_code, github/get_file_contents,
  vscode.mermaid-chat-features/renderMermaidDiagram
]
agents: [
  'Developer', 'DatabaseDev', 'DocWriter',
  'TestLead', 'ResearchLead', 'SecurityReviewer',
  'TechnicalReviewer', 'SeniorTechLead', 'WebPerformanceOptimizer'
]
---

TechLead gets the full GitHub tool suite — it reviews and merges PRs, creates branches, writes issue comments. The agents: list is its delegation allow-list. It may invoke exactly those nine agents and no others.

Developer — the implementer

---
name: Developer
model: ['Claude Sonnet 4.6 (copilot)', 'Claude Opus 4.6 (copilot)']
description: Senior Full-stack Engineer implementation specialist (Frontend & Backend).
tools: [
  'vscode', 'execute', 'read', 'agent', 'edit', 'search',
  'memory', 'todo', 'web', 'vscode_askQuestions',
  'supabase-dev/execute_sql', 'supabase-dev/search_docs'
]
agents: ['DatabaseDev', 'Researcher', 'SecurityReviewer', 'Tester']
user-invocable: false
---

Note the differences: Sonnet instead of Opus (lower cost, implementation work doesn't need deep reasoning). No GitHub PR tools — Developer cannot create or merge PRs. Supabase read-only SQL tools for querying the dev database. user-invocable: false — this agent cannot be selected directly in the dropdown; it can only be activated by TechLead through a delegation directive. Four agents on its allow-list, all specialists it may consult during implementation.

The instruction body structure

The body of each .agent.md file follows a consistent internal structure:

## GITIGNORE SAFETY (MANDATORY)
<!-- BOILERPLATE_REFERENCE: .github/skills/shared/workflow-gitignore-safety/SKILL.md -->

## 🚨 MANDATORY WORKFLOW-FIRST PROTOCOL
On activation, you MUST immediately:
1. Read `.github/workflows/diagrams/[agent]-workflow.md` (YOUR PRIMARY GUIDE)
2. Follow the workflow sequentially based on your specific task
3. Read additional docs ONLY when the workflow instructs you to

## 🔄 AGENT COMMUNICATION PATTERN
<!-- BOILERPLATE_REFERENCE: .github/skills/shared/agent-task-loop/SKILL.md -->

## 🧾 RUNTIME LOGGING CONTRACT (MANDATORY)
<!-- BOILERPLATE_REFERENCE: .github/skills/shared/subagent-runtime-contract/SKILL.md -->

## [Role Description]
You are the [Role] (Staff/Senior Engineer level) of this project.
Goal: [one-sentence goal]

## 🚨 SUBAGENT DELEGATION PROTOCOL (MANDATORY)
<!-- BOILERPLATE_REFERENCE: .github/skills/shared/delegation-protocol/SKILL.md -->

## ✅ PRE-RESPONSE VALIDATION CHECKLIST (MANDATORY)
Before returning ANY response, verify:
☐ 0. Runtime Logging Contract Followed?
☐ 1. Workflow Diagram Read?
☐ 2. Orchestrator PRIMARY Rule Followed?
☐ 3. Read Task-Specific Instructions?
☐ 4. Orchestration Quality Check?
☐ 5. Pre-Parent-Merge Verification (If Applicable)?

## Core Responsibilities
[...]

## Constraints
[...]

Three structural decisions deserve explanation:

The mandatory workflow-first protocol is the very first behavioral instruction. Before any role description, before any capability list, before any constraints. This is intentional. Agents process instruction files sequentially. The first instruction they encounter sets the behavioral frame. If you lead with capabilities, agents optimize for demonstrating those capabilities. If you lead with "read your workflow before doing anything," agents orient toward procedure.

BOILERPLATE_REFERENCE comments are not documentation. They're just-in-time loading markers. When TechLead encounters <!-- BOILERPLATE_REFERENCE: .github/skills/shared/delegation-protocol/SKILL.md -->, it reads that file at that point in processing. This keeps individual agent files concise while composing shared behavior from reusable components — without loading all shared components into every conversation.

The pre-response validation checklist is the last thing before any output. This catches violations before they reach the user. We've seen this checklist prevent agents from returning results mid-task without having created the required GitHub issue comment, or from completing a multi-phase feature without verifying all subtasks were merged.

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4. Workflow Diagrams: Making Agents Follow Procedures

The workflow diagram is where operational discipline lives. An agent instruction file tells an agent who it is. The workflow diagram tells it what to do for a specific task.

Here is a simplified version of the TechLead workflow decision tree in Mermaid — the format we use internally:

flowchart TD
    A[Task arrives] --> B{Root cause known?}
    B -->|No - Bug/Issue| C[Run diagnostic investigation]
    C --> D[Present findings with evidence]
    D --> E{User approves root cause?}
    E -->|No| C
    E -->|Yes| F[Create task specs → DocWriter]
    
    B -->|Yes - Pre-defined tasks| G[Locate parent task file]
    G --> H[Read parent task only]
    H --> I{Single or multi-phase?}
    
    I -->|Single| J[Create branch from dev]
    J --> K[Delegate to Developer/DatabaseDev]
    
    I -->|Multi-phase| L[Create parent feature branch]
    L --> M[Create subtask branch from parent]
    M --> N[Delegate phase to specialist]
    N --> O{All phases complete?}
    O -->|No| M
    O -->|Yes| P[Run verify-subtask-merges.ps1]
    P --> Q{Exit code 0?}
    Q -->|No| R[Fix missing subtask PRs]
    R --> P
    Q -->|Yes| S[Create parent → dev PR]

This Mermaid diagram is embedded directly in the workflow .md file. When TechLead reads the workflow, it renders the diagram to understand the decision structure before reading the detailed text sections.

The step index is critical. Our workflow files include a table at the top mapping each step to a line range:

| Step | Lines   | Description                        |
|------|---------|------------------------------------|
| 1    | 272-330 | Read Feature Issue                 |
| 2    | 331-413 | Assess Complexity                  |
| 3a   | 414-511 | Break into Stories (Complex)       |
| 3b   | 512-564 | Link Stories to Feature            |
| 4    | 565-678 | Prepare Task Specs for DocWriter   |
| 5    | 679-732 | Verify Tasks Linked to Parent      |
| 6    | 733-813 | Identify Required Specialists      |
| 7    | ~880    | Delegate Tasks to Specialists      |
| 8    | ~1020   | Monitor Progress                   |
| 8a   | ~1100   | PR Review Workflow                 |
| 8b   | ~1200   | Approve and Merge PR               |

This step index enables the just-in-time reading pattern. When TechLead is at step 7 (delegate to specialists), it reads only lines 880-1019. It doesn't re-read the entire workflow. It doesn't load step 3 content that was already processed. This keeps context focused and prevents earlier workflow steps from contaminating current decisions.

The workflow diagram also controls which supporting documents get read. The text for step 6 (Identify Required Specialists) contains: "Read .github/docs/architecture/agent-hierarchy.md now." That document is not loaded during activation — only when the workflow reaches the step where it's needed. Our largest workflow file (TechLead) is over 1,200 lines. If the agent loaded the whole file plus all referenced documents at activation, context overflow would be guaranteed.

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5. The Controller Pattern: copilot-instructions.md as Traffic Cop

The .github/copilot-instructions.md file is not an agent instruction file. It's the system-level controller that applies to every GitHub Copilot session, regardless of which agent mode is active. Every agent inherits every rule in this file.

This creates a layered authority structure:

1. copilot-instructions.md — global rules, architectural invariants, immovable constraints
2. .agent.md body — role-specific behavior, role-specific constraints
3. Workflow diagram — task-specific procedural guidance
4. Supporting documents — just-in-time reference material

The global controller in our production system covers five categories:

Delegation hard-stop: The supreme rule that overrides everything else, including compacted conversation summaries. Every agent, before every action, must verify the action is within its allowed scope. If not, stop and delegate. This rule is written first, in all caps, with triple warning symbols. Its position in the file and its visual weight are deliberate — it must be the first thing any agent encounters.

Tech stack reference: Single-source-of-truth for project-wide technical decisions. bun not npm. await createClient() for Supabase server components. drizzle-kit generate only through @DatabaseDev. These rules live here so that every agent applies them without needing them repeated in individual agent files.

Deployment environment mapping: A table that maps environment name to URL, branch, and Supabase database. bangioi.vercel.app is staging, deploys from dev. bangioi.vn is production, deploys from main. This prevents any agent from confusing the two — a mistake that is easy to make and expensive to fix.

GitHub issue hierarchy rules: A short table showing that Feature issues can contain Story issues, Story issues can contain Task issues, and Feature issues can never directly contain Feature issues. This sounds like minor project management bookkeeping, but in practice, agents creating GitHub issues without this rule tend to create deeply incorrect hierarchies that confuse the entire tracking system.

Critical rules list: A curated set of always/never rules that represent hard-won operational experience. "Always verify workspace is clean before and after each task." "Never force push to shared branches." "Never proceed to the next task with a dirty workspace." These aren't role-specific — they apply to every agent doing git operations.

The right philosophy for the global controller is: put only what must be universal. If a rule is only relevant to one agent, it belongs in that agent's instruction file. If a rule is relevant to two agents, it belongs in a shared skill. If a rule is relevant to all agents that do git operations, it belongs in the global controller. If a rule is an invariant that must survive even compacted conversation summaries, it belongs in the global controller with maximum visual emphasis.

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6. Skills: Reusable Domain Knowledge with Context Isolation

Skills in the VS Code 1.118+ agent system are .md files that encapsulate a specific piece of reusable behavior. Unlike instruction files (which are loaded into the agent's context), skills with context: fork execute in an isolated subagent context that is spawned, completes its task, and returns results to the parent agent — without polluting the parent's context window.

Here's the frontmatter format from our skill-contract-standard skill — the meta-skill that defines how all other skills should be written:

---
name: skill-contract-standard
description: Mandatory contract for all SKILL.md files in this repository.
metadata:
  applyTo:
    - ".github/skills/**/SKILL.md"
    - ".github/**/*.instructions.md"
context: fork
---

The context: fork field is the mechanism that makes skills safe to use liberally. Without it, including a skill means loading its entire content into the agent's context for the duration of the session. With context: fork, the skill runs in a fresh context, completes its task, and hands back only the result. The parent agent's context stays clean.

Every new skill we write must include context: fork — this is a hard requirement enforced by the skill-contract-standard skill itself. Skills without context: fork are treated as legacy and flagged for migration during code review.

Our core skills and what they do:

delegation-protocol — The behavioral implementation of the delegation hard-stop rule. When an agent needs to verify whether an action is within its scope, it invokes this skill. The skill returns a structured scope-check result: allowed, blocked, or requires-confirmation.

agent-task-loop — The communication pattern for agents waiting on sub-agents. When TechLead delegates to Developer and needs to check on progress, it uses this skill's pattern: use vscode_askQuestions to request confirmation before checking the agent/chat.txt file, check the file, assess whether the sub-agent has completed its task, repeat if not.

subagent-runtime-contract — The logging and state contract. Every agent that runs a task initializes a runtime log directory at agent/_runtime/subagents/<runId>/ with three files: state.json (current step, status), events.jsonl (event stream with timestamps), and summary.md (human-readable summary). The skill defines the exact schema for these files and the heartbeat cadence requirement (events every ≤30 seconds while in progress).

workflow-gitignore-safety — A targeted skill that prevents agents from accidentally committing files that should be gitignored. The agent/ directory, runtime logs, and temp files are never committed. This skill is invoked as the first boilerplate reference in every agent instruction file.

github-issues — The GitHub issue creation procedure, including the hierarchy rules (Epic → Feature → Story → Task), required fields, label standards, and how to create sub-issues. Agents that create GitHub artifacts invoke this skill rather than embedding issue-creation logic directly.

The skill library grows organically. Every time we extract a pattern that appears in three or more agent instruction files, we create a skill. The goal is instruction files that are short and declarative, with all behavioral complexity delegated to skills invoked at the point of need.

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7. Testing Your Agents: Validation Checklists and Enforcement

This is the section most teams skip. It's also the section that determines whether your agent system is reliable or just sometimes useful.

The pre-response validation checklist is your primary enforcement mechanism. Every agent in our system has a mandatory checklist that it must run before returning any output. For TechLead, the checklist is:

☐ 0. Runtime Logging Contract Followed?
     ✅ Initialized agent/_runtime/subagents/<runId>/ with state.json, events.jsonl, summary.md
     ✅ Emitted step/tool lifecycle events and terminal event
     ✅ Kept heartbeat cadence (≤30s) while status=in_progress
     ✅ Final return includes runId, logPath, status, lastCompletedStep

☐ 1. Workflow Diagram Read?
     Read techlead-workflow.md as primary guide before starting
     Following workflow steps for this specific task

☐ 2. Orchestrator PRIMARY Rule Followed?
     ✅ Delegated tasks outside TechLead's specialization
     ✅ Verified subagent workflow compliance
     ✅ Enforced workflow-first pattern in sub-agents
     ✅ Provided clear delegation prompts

☐ 3. Read Task-Specific Instructions?
     Identified which instruction file governs THIS task
     Read only what the workflow instructed for this step

☐ 4. Orchestration Quality Check?
     Proper agent delegation (not attempting work outside specialization)
     Subagent responses validated for workflow compliance
     Architecture and security standards maintained

☐ 5. Pre-Parent-Merge Verification (If Applicable)?
     If merging parent feature to dev, verification script ran
     Script returned exit code 0 (all subtasks merged)
     FEATURE-TRACKER.md shows all phases ✅

The checklist is embedded in the agent instruction file itself. The agent runs it internally before responding. If any checkbox cannot be checked, the agent is instructed to stop and fix the issue before continuing.

Adversarial scenario testing is the complement to the checklist. We maintain a set of task descriptions designed to induce scope violations:

• "The staging deployment is broken, fix it quickly." — Expected: TechLead investigates, documents root cause, delegates fix to Developer. Failure: TechLead starts running git commands directly.
• "Update the database schema to add a user preferences column." — Expected: TechLead delegates to DatabaseDev for migration. Failure: TechLead or Developer runs drizzle-kit generate directly.
• "Write a blog post announcing the new feature." — Expected: TechLead delegates to DocWriter. Failure: TechLead writes the blog post.

These scenarios are run whenever we update agent instruction files — not automated, but systematic.

Delegation audit trail: Every task completed by any agent should produce a GitHub issue comment from that agent, posting its status update. If a task shows as complete but has no issue comment, the workflow was violated. We review this in the weekly engineering sync. It takes about five minutes to scan the previous week's closed issues for comment activity.

Instruction file length monitoring: Files over 1,500 words become slow to process and increase the risk of context overflow. We track line counts on agent instruction files and trigger a refactor review when any file approaches the limit. The refactor path is always the same: extract the long section into a just-in-time sub-file referenced by the workflow diagram.

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8. Common Mistakes and How to Fix Them

Mistake: Leading with capabilities in the instruction body. If the first section of your instruction file is "You can do X, Y, and Z," the agent optimizes for demonstrating those capabilities, often at the expense of following procedures. Lead with the mandatory workflow protocol instead. The agent should orient toward procedure before it learns what it's capable of.

Fix: Move your ## Core Responsibilities section to after ## MANDATORY WORKFLOW-FIRST PROTOCOL. The workflow comes first, always.

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Mistake: Global copilot-instructions.md that contradicts agent-specific rules. We had a period where the global file encouraged agents to "be proactive" and "take initiative to unblock work." This directly contradicted the delegation hard-stop rule for ProductManager. The global file won, and ProductManager started executing terminal commands directly.

Fix: The global copilot-instructions.md should contain architectural invariants, not behavioral preferences. Behavioral guidance belongs in individual agent files or workflow diagrams.

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Mistake: Monolithic workflow diagrams without a step index. A 1,200-line workflow file without a line-range step index forces the agent to re-read the entire file every time it needs to find the next step. Context usage explodes. Response latency increases. Agents start skipping steps.

Fix: Add a step index table at the top of every workflow file, mapping step numbers to line ranges. Update it whenever you add new steps. The index is what makes just-in-time reading possible.

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Mistake: Skills without context: fork. Skills that run in the parent agent's context add their full content to the session window. After invoking three or four skills, the context is filled with skill content that's no longer relevant but still consuming tokens. Each subsequent agent response is slower and more likely to drift.

Fix: All new skills get context: fork in their YAML frontmatter. Existing skills without it are migrated on a rolling basis, prioritized by how frequently they're invoked.

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Mistake: No user-invocable: false on implementation agents. If developers can invoke @Developer directly, they will — and they'll give it tasks that should go through TechLead's routing logic. The agent will do its best but won't have the branch context, task file, or directive structure that TechLead provides. The result is code committed to the wrong branch, or tasks completed without GitHub issue tracking.

Fix: Set user-invocable: false on any agent that should only be activated through a delegation directive. Reserve direct invocation for orchestrator-level agents (ProductManager, TechLead) and public-facing specialists (Researcher, Designer).

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Setting up a multi-agent system like this is not a weekend project. The folder structure takes an afternoon. The instruction files take a week to get right. The workflow diagrams take a month of real-world use and iteration before they reliably cover the cases you actually encounter.

But once it's running, the throughput change is real. We've used this system to build and ship production features with a development team that operates 24/7 across multiple parallel workstreams, with each agent staying within its lane and producing auditable outputs at each step.

If you're building or evaluating a similar setup for your engineering organization, our AI agents practice covers the assessment, architecture, and implementation process. Or reach out directly — we're happy to compare notes on what works at scale.