Feature/tas27

.github/agents/speckit.contentpack.agent.md

@@ -0,0 +1,273 @@
+---
+description: Create a new content pack (agent team + data + knowledge base) using the reference template and step-by-step guide.
+---
+
+# 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:
+
+| Question | Design decision |
+|----------|----------------|
+| Does the team need to look up data? | Add a **ResearchAgent** with `use_knowledge_base: true` |
+| Does it need to generate images? | Add an agent with `use_toolbox: true` and the appropriate `toolbox_filter` |
+| Should it ask the user clarifying questions? | Set `user_responses: true` on the relevant agent |
+| Are there multiple distinct tasks? | Add specialist agents and a **TriageAgent** to route between them |
+| Is it a simple Q&A over data? | A single agent with KB access may be enough — no triage needed |
+
+**Examples from this repo:**
+
+| Pack | Agents | Why |
+|------|--------|-----|
+| `content_gen` | Triage → Planning → Research → TextContent → ImageContent → Compliance | Complex creative workflow with multiple output types |
+| `contract_compliance` | Triage → Research → Analysis | Document review with KB lookup |
+| `hr_onboarding` | Single team (no KB) | Workflow-only, no data grounding needed |
+
+**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:**
+```jsonc
+{
+  "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-4.1-mini",
+  ...
+}
+```
+
+**Each agent must include `type`:**
+```jsonc
+{
+  "input_key": "my_agent",
+  "type": "",                    // REQUIRED — empty string is fine, but field must exist
+  "name": "MyAgent",
+  ...
+}
+```
+
+**Each starting task must include `created`, `creator`, `logo`:**
+```jsonc
+"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:
+
+| Format | How it's indexed |
+|--------|-----------------|
+| CSV | One document per row. Columns become searchable fields. |
+| PDF/DOCX | Uploaded to blob; use `blob_indexes` in `pack.json` for document-crack indexing. |
+
+### Step 2 — Create `pack.json`
+
+```jsonc
+{
+  "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/seed_knowledge_bases.py` in the `KNOWLEDGE_BASES`
+dict. Place it before the `# ── Example Pack ──` comment block:
+
+```python
+    # ── Your Pack ──
+    "your-pack-data-kb": {
+        "description": "What this KB provides",
+        "model": {
+            "kind": "azureOpenAI",
+            "azureOpenAIParameters": {
+                "resourceUri": AI_SERVICES_ENDPOINT,
+                "deploymentId": "gpt-4.1-mini",
+                "modelName": "gpt-4.1-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):**
+
+| Item | Pattern | Example |
+|------|---------|---------|
+| KB name (dict key) | `<pack>-<purpose>-kb` | `pet-food-catalog-kb` |
+| Knowledge Source name | `<pack>-<purpose>-ks` | `pet-food-catalog-ks` |
+| Search index name | `<pack>-<purpose>-index` | `pet-food-catalog-index` |
+
+### Step 4 — Connect agents to the KB
+
+In your team config JSON, set these fields on the agent that needs search:
+
+```jsonc
+"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/Selecting-Team-Config-And-Data.ps1`. Search for `NEW CONTENT PACK`
+— each insertion point has a comment template. There are **4 things** to do:
+
+| # | What | Where |
+|---|------|-------|
+| 1 | Add `Write-Host "N. Your Pack Name"` | Menu display section |
+| 2 | Add `elseif ($useCaseSelection -eq "N") { ... }` | Selection handler |
+| 3 | Add deployment block (team config upload + `Deploy-ContentPack`) | After Content Gen block |
+| 4 | Add `-or $useCaseSelection -eq "N"` to network/KB/success conditions | Only if pack has data |
+
+After adding your entry, update `$allOption` to `N + 1` so "All" is always last.
+
+---
+
+## Agent Tool Options Reference
+
+| Field | Type | Purpose |
+|-------|------|---------|
+| `use_knowledge_base` | bool | Connects a Foundry IQ KB as an MCP search tool |
+| `knowledge_base_name` | string | Name of the KB (must exist in `seed_knowledge_bases.py`) |
+| `use_file_search` | bool | Attaches an Azure AI vector store for file-level RAG |
+| `vector_store_name` | string | Name of the vector store in Foundry |
+| `use_toolbox` | bool | Connects MCP toolbox tools (e.g., `generate_marketing_image`) |
+| `toolbox_filter` | string | Tag filter for which toolbox tools are available |
+| `coding_tools` | bool | Enables code interpreter sandbox |
+| `user_responses` | bool | Allows the agent to pause and ask the user a question |
+
+> **`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
+
+```bash
+# 1. Deploy infrastructure
+azd up
+
+# 2. Provision pack resources — select your pack or "All"
+./infra/scripts/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`.
+

.github/prompts/speckit.contentpack.prompt.md

@@ -0,0 +1,3 @@
+---
+agent: speckit.contentpack
+---

.gitignore

@@ -1,3 +1,9 @@
+# Local specifications / task tracking
+localspec/
+
+# Azurite local storage emulator
+__azurite_db_*
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]
@@ -126,6 +132,7 @@ celerybeat.pid
 # Environments
 .env
 .venv
+.venv-*
 scriptenv
 env/
 venv/
@@ -464,4 +471,9 @@ __pycache__/
 data/sample_code/
 # Bicep local files
 *.local*.bicepparam
-*.local*.parameters.json
+*.local*.parameters.json
+
+# Compiled ARM template (regenerated by azd provision)
+infra/main.json
+# Preflight check artifacts
+infra/preflight-*.bicepparam

README.md

@@ -89,7 +89,7 @@ Follow the quick deploy steps on the deployment guide to deploy this solution to
 
 To deploy this solution accelerator, ensure you have access to an [Azure subscription](https://azure.microsoft.com/free/) with the necessary permissions to create **resource groups and resources**. Follow the steps in [Azure Account Set Up](./docs/AzureAccountSetUp.md).
 
-Check the [Azure Products by Region](https://azure.microsoft.com/en-us/explore/global-infrastructure/products-by-region/table) page and select a **region** where the following services are available: Azure OpenAI Service, Azure AI Search, and Azure Semantic Search.
+Check the [Azure Products by Region](https://azure.microsoft.com/en-us/explore/global-infrastructure/products-by-region/table) page and select a **region** where the following services are available: Microsoft Foundry Service, Foundry Models, Azure AI Search, and Search Semantic Ranker.
 
 Here are some example regions where the services are available: East US, East US2, Japan East, UK South, Sweden Central.
 
@@ -98,7 +98,8 @@ Pricing varies per region and usage, so it isn't possible to predict exact costs
 Use the [Azure pricing calculator](https://azure.microsoft.com/en-us/pricing/calculator) to calculate the cost of this solution in your subscription. [Review a sample pricing sheet for the architecture](https://azure.com/e/86d0eefbe4dd4a23981c1d3d4f6fe7ed).
 | Product | Description | Cost |
 |---|---|---|
-| [Azure OpenAI Service](https://learn.microsoft.com/azure/ai-services/openai/) | Powers the AI agents for task automation | [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/openai-service/) |
+| [Microsoft Foundry]( https://learn.microsoft.com/en-us/azure/foundry/) | Powers the AI agents for task automation | [Pricing](https://azure.microsoft.com/en-us/pricing/details/microsoft-foundry/) |
+| [Azure AI Search](https://learn.microsoft.com/en-us/azure/search/) | Powers vector based data search | [Pricing]( https://azure.microsoft.com/en-us/pricing/details/search/) |
 | [Azure Container Apps](https://learn.microsoft.com/azure/container-apps/) | Hosts the web application frontend | [Pricing](https://azure.microsoft.com/pricing/details/container-apps/) |
 | [Azure Cosmos DB](https://learn.microsoft.com/azure/cosmos-db/) | Stores metadata and processing results | [Pricing](https://azure.microsoft.com/pricing/details/cosmos-db/) |
 | [Azure Container Registry](https://learn.microsoft.com/azure/container-registry/) | Stores container images for deployment | [Pricing](https://azure.microsoft.com/pricing/details/container-registry/) |
@@ -159,6 +160,7 @@ By using the *Multi-Agent Custom Automation Engine* solution accelerator, users
 | Retail Remediation | Customer Success Manager | This approach is seeking a smarter way to manage workflows and ensure customer satisfaction. Challenges include difficulty engaging the right agents, inefficiencies from manual processes, fragmented data, and limited security controls. | Using the Multi-Agent Custom Automation Engine, the Customer Success Manager recruits intelligent agents to analyze satisfaction and recommend steps for remediation. The platform’s modular design and reasoning capabilities allow for analyzing dependencies, planning transitions, and reducing manual rework. |
 | RFP Reviewer  | VP of Finance  | Reviewing RFPs manually under tight deadlines is challenging. Compliance checks and risk assessments slow progress, leaving critical gaps and delaying decisions. | Multi-Agent RFP Reviewer is an intelligent platform addresses these issues by deploying AI agents to review RFPs, identify risks, recommend remediation, and execute actions seamlessly - transforming a labor-intensive process into a fast, accurate, and compliant workflow. |
 | Contract Compliance Reviewer | Compliance Counsel  | Reviewing Contracts for compliance manually under tight deadlines is challenging. Compliance checks and risk assessments slow progress, leaving critical gaps and delaying decisions. | Multi-Agent Contract Compliance Reviewer is an intelligent platform addresses these issues by deploying AI agents to review Contracts, identify risks, recommend remediation, and execute actions seamlessly - transforming a labor-intensive process into a fast, accurate, and compliant workflow. |
+| Content Generation | Marketing Creative | Accelerate your marketing content creation by leveraging AI to interpret creative briefs and generate on-brand, multimodal content. The solution helps marketing teams reduce time-to-market for campaigns by automating the creation of compliant marketing copy and images grounded in product data. | This solution leverages a team of agents to interpret creative briefs, retrieve product information, generate marketing content, and validate brand compliance. |
 
 </details>