Build a Reactive AI Pipeline

In this quickstart, you’ll build a multi-agent research team with web search, an MCP tool server, a knowledge base backed by vector search, and persistent storage. You’ll see how Pragmatiks resolves complex dependency graphs and automatically propagates changes.

Time estimate: 45 minutes. Assumes you’ve completed the amateur quickstart.

What you’ll build

15 new resources, wired together, plus the cluster access and namespace from the amateur quickstart. Pragmatiks resolves the entire dependency graph in the correct order.

Prerequisites

Completed the amateur quickstart (CLI installed, authenticated)
An OpenAI API key (for embeddings and GPT-4o)
A running Qdrant instance (Qdrant Cloud free tier works)
The my-cluster Kubernetes config and agents namespace from the amateur quickstart

Step 1: Secrets

Store all API keys as pragma secrets. Create secrets.yaml:

secrets.yaml

provider: pragma
resource: secret
name: anthropic-key
config:
  data:
    ANTHROPIC_API_KEY: "sk-ant-your-key-here"
---
provider: pragma
resource: secret
name: openai-key
config:
  data:
    OPENAI_API_KEY: "sk-your-openai-key-here"
---
provider: pragma
resource: secret
name: qdrant-key
config:
  data:
    QDRANT_API_KEY: "your-qdrant-api-key"

pragma resources apply secrets.yaml

Step 2: AI models

Configure two models from different providers. Create models.yaml:

models.yaml

provider: agno
resource: models/anthropic
name: claude
config:
  id: claude-sonnet-4-5-20250929
  api_key:
    provider: pragma
    resource: secret
    name: anthropic-key
    field: outputs.ANTHROPIC_API_KEY
---
provider: agno
resource: models/openai
name: gpt4o
config:
  id: gpt-4o
  api_key:
    provider: pragma
    resource: secret
    name: openai-key
    field: outputs.OPENAI_API_KEY

Each model’s api_key is a field reference pointing to a secret output. The key value is injected automatically at provisioning time.

pragma resources apply models.yaml

Step 3: Tools

Give agents capabilities through tools. Create tools.yaml:

tools.yaml

provider: agno
resource: tools/websearch
name: search
config:
  enable_search: true
  enable_news: true
  backend: auto
---
provider: agno
resource: tools/mcp
name: filesystem
config:
  command: "npx -y @modelcontextprotocol/server-filesystem /data"

The web search tool gives agents internet access. The MCP tool connects to a Model Context Protocol server — in this case, a filesystem server for reading local files.

pragma resources apply tools.yaml

Step 4: Knowledge base

Set up vector search so agents can query a knowledge base. This creates four connected resources: an embedder, a Qdrant collection, a vector database configuration, and the knowledge base itself. Create knowledge.yaml:

knowledge.yaml

provider: agno
resource: knowledge/embedder/openai
name: embedder
config:
  id: text-embedding-3-small
  api_key:
    provider: pragma
    resource: secret
    name: openai-key
    field: outputs.OPENAI_API_KEY
---
provider: qdrant
resource: collection
name: knowledge-base
config:
  url: "https://your-instance.cloud.qdrant.io:6333"
  api_key:
    provider: pragma
    resource: secret
    name: qdrant-key
    field: outputs.QDRANT_API_KEY
  name: knowledge-base
  vectors:
    size: 1536
    distance: Cosine
---
provider: agno
resource: vectordb/qdrant
name: embeddings
config:
  url: "https://your-instance.cloud.qdrant.io:6333"
  collection:
    provider: qdrant
    resource: collection
    name: knowledge-base
    field: outputs.name
  api_key:
    provider: pragma
    resource: secret
    name: qdrant-key
    field: outputs.QDRANT_API_KEY
  embedder:
    provider: agno
    resource: knowledge/embedder/openai
    name: embedder
---
provider: agno
resource: knowledge
name: docs
config:
  vector_db:
    provider: agno
    resource: vectordb/qdrant
    name: embeddings
  max_results: 5

Notice the layered dependencies:

The embedder references the OpenAI secret
The collection creates the knowledge-base collection in Qdrant, sized for the embedder’s 1536-dimensional vectors
The vector database references the embedder (dependency), the collection’s name, and the Qdrant secret (field references)
The knowledge base references the vector database

pragma resources apply knowledge.yaml

Step 5: Agents

Define two specialized agents with different models and capabilities. Create agents.yaml:

agents.yaml

provider: agno
resource: agent
name: researcher
config:
  description: "Research specialist with web search and knowledge base access"
  model:
    provider: agno
    resource: models/anthropic
    name: claude
  instructions:
    - "You are a research specialist."
    - "Search the web for current information."
    - "Check the knowledge base for internal documents."
    - "Cite your sources."
  tools:
    - provider: agno
      resource: tools/websearch
      name: search
    - provider: agno
      resource: tools/mcp
      name: filesystem
  knowledge:
    provider: agno
    resource: knowledge
    name: docs
  markdown: true
---
provider: agno
resource: agent
name: writer
config:
  description: "Technical writer that produces clear documentation"
  model:
    provider: agno
    resource: models/openai
    name: gpt4o
  instructions:
    - "You are a technical writer."
    - "Take research findings and produce clear, structured documentation."
    - "Use headings, bullet points, and code examples."
  markdown: true

The researcher agent uses Claude with web search, MCP tools, and knowledge base access. The writer agent uses GPT-4o with a focused writing prompt. Each agent declares its model as a dependency, so model changes propagate automatically.

pragma resources apply agents.yaml

Step 6: Team

Combine agents into a team. Create team.yaml:

team.yaml

provider: agno
resource: team
name: research-team
config:
  description: "Research team that finds information and produces documentation"
  members:
    - provider: agno
      resource: agent
      name: researcher
    - provider: agno
      resource: agent
      name: writer
  instructions:
    - "Coordinate research and writing tasks."
    - "The researcher finds information, the writer produces the final output."
  markdown: true

The team references both agents as dependencies in its members list. When either agent changes, the team rebuilds.

pragma resources apply team.yaml

Step 7: Deploy and observe

Deploy the team to the agents namespace of your cluster. Create runner.yaml:

runner.yaml

provider: agno
resource: runner
name: research-team
config:
  teams:
    - provider: agno
      resource: team
      name: research-team
  config:
    provider: kubernetes
    resource: config
    name: my-cluster
  namespace:
    provider: kubernetes
    resource: namespace
    name: agents

pragma resources apply runner.yaml

Watch the resources resolve:

pragma resources list

All 15 resources should reach READY state. Pragmatiks resolved the dependency graph — secrets first, then models, tools, and the collection, then agents, then the team, then the runner.

Step 8: Change propagation

This is where reactive dependencies shine. Swap the researcher’s model from Claude to GPT-4o and watch the cascade. Update agents.yaml — change the researcher’s model reference:

provider: agno
resource: agent
name: researcher
config:
  description: "Research specialist with web search and knowledge base access"
  model:
    provider: agno
    resource: models/openai
    name: gpt4o
  instructions:
    - "You are a research specialist."
    - "Search the web for current information."
    - "Check the knowledge base for internal documents."
    - "Cite your sources."
  tools:
    - provider: agno
      resource: tools/websearch
      name: search
    - provider: agno
      resource: tools/mcp
      name: filesystem
  knowledge:
    provider: agno
    resource: knowledge
    name: docs
  markdown: true

pragma resources apply agents.yaml

Now watch what happens:

pragma resources list

The researcher agent rebuilds with the new model
The research-team automatically rebuilds because its member changed
The runner redeploys because the team spec changed

You changed one line, and Pragmatiks propagated the change through three resources. No manual coordination needed.

How it works

Two mechanisms make this possible: Dependencies (Dependency[T]) link resources together. When a resource declares a dependency on another, Pragmatiks tracks the relationship. If the upstream resource changes, the dependent rebuilds.

# This declares: "my agent depends on the claude model"
model:
  provider: agno
  resource: models/anthropic
  name: claude

Field references (Field[T]) inject specific output values from other resources. The referenced value is resolved at provisioning time and re-resolved when the source changes.

# This declares: "inject the ANTHROPIC_API_KEY output from the secret"
api_key:
  provider: pragma
  resource: secret
  name: anthropic-key
  field: outputs.ANTHROPIC_API_KEY

Resources can be applied in any order. Pragmatiks holds unresolved resources in WAITING state until their dependencies are ready, then processes them automatically.

Full YAML

Complete multi-document YAML (all 15 resources)

pipeline.yaml

# --- Secrets ---
provider: pragma
resource: secret
name: anthropic-key
config:
  data:
    ANTHROPIC_API_KEY: "sk-ant-your-key-here"
---
provider: pragma
resource: secret
name: openai-key
config:
  data:
    OPENAI_API_KEY: "sk-your-openai-key-here"
---
provider: pragma
resource: secret
name: qdrant-key
config:
  data:
    QDRANT_API_KEY: "your-qdrant-api-key"
---
# --- Models ---
provider: agno
resource: models/anthropic
name: claude
config:
  id: claude-sonnet-4-5-20250929
  api_key:
    provider: pragma
    resource: secret
    name: anthropic-key
    field: outputs.ANTHROPIC_API_KEY
---
provider: agno
resource: models/openai
name: gpt4o
config:
  id: gpt-4o
  api_key:
    provider: pragma
    resource: secret
    name: openai-key
    field: outputs.OPENAI_API_KEY
---
# --- Tools ---
provider: agno
resource: tools/websearch
name: search
config:
  enable_search: true
  enable_news: true
  backend: auto
---
provider: agno
resource: tools/mcp
name: filesystem
config:
  command: "npx -y @modelcontextprotocol/server-filesystem /data"
---
# --- Knowledge ---
provider: agno
resource: knowledge/embedder/openai
name: embedder
config:
  id: text-embedding-3-small
  api_key:
    provider: pragma
    resource: secret
    name: openai-key
    field: outputs.OPENAI_API_KEY
---
provider: qdrant
resource: collection
name: knowledge-base
config:
  url: "https://your-instance.cloud.qdrant.io:6333"
  api_key:
    provider: pragma
    resource: secret
    name: qdrant-key
    field: outputs.QDRANT_API_KEY
  name: knowledge-base
  vectors:
    size: 1536
    distance: Cosine
---
provider: agno
resource: vectordb/qdrant
name: embeddings
config:
  url: "https://your-instance.cloud.qdrant.io:6333"
  collection:
    provider: qdrant
    resource: collection
    name: knowledge-base
    field: outputs.name
  api_key:
    provider: pragma
    resource: secret
    name: qdrant-key
    field: outputs.QDRANT_API_KEY
  embedder:
    provider: agno
    resource: knowledge/embedder/openai
    name: embedder
---
provider: agno
resource: knowledge
name: docs
config:
  vector_db:
    provider: agno
    resource: vectordb/qdrant
    name: embeddings
  max_results: 5
---
# --- Agents ---
provider: agno
resource: agent
name: researcher
config:
  description: "Research specialist with web search and knowledge base access"
  model:
    provider: agno
    resource: models/anthropic
    name: claude
  instructions:
    - "You are a research specialist."
    - "Search the web for current information."
    - "Check the knowledge base for internal documents."
    - "Cite your sources."
  tools:
    - provider: agno
      resource: tools/websearch
      name: search
    - provider: agno
      resource: tools/mcp
      name: filesystem
  knowledge:
    provider: agno
    resource: knowledge
    name: docs
  markdown: true
---
provider: agno
resource: agent
name: writer
config:
  description: "Technical writer that produces clear documentation"
  model:
    provider: agno
    resource: models/openai
    name: gpt4o
  instructions:
    - "You are a technical writer."
    - "Take research findings and produce clear, structured documentation."
    - "Use headings, bullet points, and code examples."
  markdown: true
---
# --- Team ---
provider: agno
resource: team
name: research-team
config:
  description: "Research team that finds information and produces documentation"
  members:
    - provider: agno
      resource: agent
      name: researcher
    - provider: agno
      resource: agent
      name: writer
  instructions:
    - "Coordinate research and writing tasks."
    - "The researcher finds information, the writer produces the final output."
  markdown: true
---
# --- Deployment ---
provider: agno
resource: runner
name: research-team
config:
  teams:
    - provider: agno
      resource: team
      name: research-team
  config:
    provider: kubernetes
    resource: config
    name: my-cluster
  namespace:
    provider: kubernetes
    resource: namespace
    name: agents

pragma resources apply pipeline.yaml

Getting started

Concepts

Guides

Troubleshooting

Build a Reactive AI Pipeline

What you’ll build

Prerequisites

Step 1: Secrets

Step 2: AI models

Step 3: Tools

Step 4: Knowledge base

Step 5: Agents

Step 6: Team

Step 7: Deploy and observe

Step 8: Change propagation

How it works

Full YAML

Next steps

Create a Custom Provider

Reactive Dependencies

​What you’ll build

​Prerequisites

​Step 1: Secrets

​Step 2: AI models

​Step 3: Tools

​Step 4: Knowledge base

​Step 5: Agents

​Step 6: Team

​Step 7: Deploy and observe

​Step 8: Change propagation

​How it works

​Full YAML

​Next steps

Create a Custom Provider

Reactive Dependencies

What you’ll build

Prerequisites

Step 1: Secrets

Step 2: AI models

Step 3: Tools

Step 4: Knowledge base

Step 5: Agents

Step 6: Team

Step 7: Deploy and observe

Step 8: Change propagation

How it works

Full YAML

Next steps