Custom agent imported from microsoft/Multi-Agent-Custom-Automation-Engine-Solution-Accelerator (.github/agents/speckit.contentpack.agent.md). Copyright stays with the author.

Content Packs

Optional, drop-in extensions to the Multi-Agent Custom Automation Engine. A pack ships everything needed to add a domain-specific agent team without touching core code.

The core solution works fine when the content_packs/ folder is empty or absent.

Folder Structure

content_packs/
└── <pack_name>/
    ├── pack.json               # optional — declares search indexes + blob uploads
    ├── agent_teams/
    │   └── *.json              # required — one or more team config files (any name)
    ├── datasets/               # optional — source data for grounding
    │   ├── data/*.csv
    │   └── docs/*.pdf
    └── scripts/                # optional — pack-local utilities

• <pack_name> should be lowercase snake_case (e.g. pet_food, legal_review).
• JSON files inside agent_teams/ can be named anything — the upload script globs all *.json files in that directory.

Designing Your Agent Team

Every pack needs at least one team config JSON in agent_teams/. The agents you create should be tailored to your domain — there is no fixed pattern. Consider:

What does the user need? Design agents around the tasks your users will perform, not around a template. Ask yourself:

Examples from this repo:

Key rules:

• Every agent needs a unique input_key (used for routing between agents).
• The team_id must be a valid UUID using only hex characters (0-9, a-f). Use the pattern 00000000-0000-0000-0000-00000000NNNN where NNNN is unique.
• At least one starting_tasks entry is required (the example prompt shown in the UI).

Required Fields (validation will reject uploads without these)

Team-level:

{
  "id": "1",
  "team_id": "00000000-0000-0000-0000-000000000008",
  "name": "Your Team Name",
  "status": "visible",           // REQUIRED — team won't appear in UI without this
  "deployment_name": "gpt-5.4-mini",
  ...
}

Each agent must include type:

{
  "input_key": "my_agent",
  "type": "",                    // REQUIRED — empty string is fine, but field must exist
  "name": "MyAgent",
  ...
}

Each starting task must include created, creator, logo:

"starting_tasks": [
  {
    "id": "task-1",
    "name": "Example Task",
    "prompt": "A sample prompt users can click to start",
    "created": "",              // REQUIRED — empty string is fine
    "creator": "",              // REQUIRED — empty string is fine
    "logo": ""                  // REQUIRED — empty string is fine
  }
]

Missing any of these fields results in a 400 error during upload. Use an existing pack (e.g. hr_onboarding/agent_teams/hr.json) as a reference for the full required schema.

Data & Knowledge Bases

If your agents need to search domain-specific data, you need three things wired together:

CSV/PDF ──► AI Search Index ──► Knowledge Base (MCP) ──► Agent
            (pack.json)         (seed_knowledge_bases.py)  (agent_teams/*.json)

Step 1 — Add your data

Put source files in datasets/data/. Supported formats:

Step 2 — Create pack.json

{
  "name": "your_pack",
  "description": "What this pack does",
  "search_indexes": [
    {
      "index_name": "your-pack-data-index",
      "csv_path": "datasets/data/your_data.csv",
      "key_field": "id",
      "title_field": "product_name"
    }
  ],
  "blob_uploads": [
    {
      "container": "your-pack-dataset",
      "source": "datasets/data",
      "pattern": "*.csv"
    }
  ]
}

The index_name must be globally unique on your search service and will be referenced in the KB registration.

Step 3 — Register the Knowledge Base

Add an entry to infra/scripts/post-provision/seed_knowledge_bases.py in the KNOWLEDGE_BASES dict. Place it before the # ── Example Pack ── comment block:

    # ── Your Pack ──
    "your-pack-data-kb": {
        "description": "What this KB provides",
        "model": {
            "kind": "azureOpenAI",
            "azureOpenAIParameters": {
                "resourceUri": AI_SERVICES_ENDPOINT,
                "deploymentId": "gpt-5.4-mini",
                "modelName": "gpt-5.4-mini",
            },
        },
        "sources": [
            {
                "name": "your-pack-data-ks",
                "description": "What the source data contains",
                "index_name": "your-pack-data-index",  # Must match pack.json
                "searchable_fields": ["content", "title"],
            },
        ],
    },

Naming convention (must be consistent across files):

Step 4 — Connect agents to the KB

In your team config JSON, set these fields on the agent that needs search:

"use_knowledge_base": true,
"knowledge_base_name": "your-pack-data-kb"  // Must match the key in seed_knowledge_bases.py

The agent's system_message should instruct it to always use the search tool and never hallucinate data.

Registering in the Deployment Script

Edit infra/scripts/post-provision/Selecting-Team-Config-And-Data.ps1. Search for NEW CONTENT PACK — each insertion point has a comment template. There are 4 things to do:

After adding your entry, update $allOption to N + 1 so "All" is always last.

Agent Tool Options Reference

user_responses guidance: Default to false unless the user explicitly asks for the agent to collect human feedback or ask clarifying questions. When false, the agent runs autonomously without pausing for input. Only set to true on agents whose design requires them to ask the user a question mid-workflow (e.g., an intake agent gathering requirements).

Deploying

# 1. Deploy infrastructure
azd up

# 2. Provision pack resources — select your pack or "All"
./infra/scripts/post-provision/Selecting-Team-Config-And-Data.ps1 -ResourceGroup <rg>

Checklist

• content_packs/<pack>/agent_teams/ has at least one valid JSON team config
• team_id is a valid hex UUID (0-9, a-f only)
• starting_tasks has at least one example prompt
• If using a KB: pack.json exists with matching index_name
• If using a KB: datasets/data/ contains the source files
• If using a KB: entry added to seed_knowledge_bases.py with matching names
• If using a KB: agent has use_knowledge_base: true + correct knowledge_base_name
• Agent system_message tells it to search (not hallucinate)
• Pack registered in Selecting-Team-Config-And-Data.ps1 (all 4 locations)
• $allOption updated if you added a new menu number

Removing a Pack

Delete the pack folder. Previously uploaded team configs remain in Cosmos until deleted via DELETE /api/v4/team_configs/{team_id}. Search indexes and blob containers are also left in place — clean up with az search / az storage.