> ## Documentation Index
> Fetch the complete documentation index at: https://docs.e6data.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Architecture

> The logical and physical views of an AgentFlow deployment: what each component does and what actually runs.

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<DocMeta type="explanation" applies="All deployments" />

## Logical view

<Frame caption="AgentFlow logical view: components and responsibilities. Purple: workflow and model control path. Grey: data, audit and integration paths.">
  <img src="https://mintcdn.com/e6data/y-HeszjEBPpoJsYY/images/agentflow/logical-view.png?fit=max&auto=format&n=y-HeszjEBPpoJsYY&q=85&s=c303806d155fc545231693293d2c7bdc" alt="AgentFlow logical architecture. The customer's durable storage at the top, users and event sources on the left, the AgentFlow runtime in the center, external AI models on the right, and the e6data query engine and outbound delivery at the bottom." width="2481" height="1791" data-path="images/agentflow/logical-view.png" />
</Frame>

An AgentFlow deployment has a central runtime and four areas around it:

| Area | What it holds |
| - | - |
| Your durable storage (top) | Versioned workflow specs, skill library, Agent Tools, trigger configs, project and catalog memory, semantic ontology, metric definitions, run history, action audit log, schema indexes and source data tables |
| Your users and event sources (left) | Internal and external users through the browser UI, HTTP API and SSE; external systems such as ERP, MES and CMMS through webhooks |
| AgentFlow runtime (center) | Interface layer, workflow compiler, per-step scoped agents, subagent runtime, trigger handling, action engine, memory and checkpoints, tool catalog, skill library, semantic layer, code sandbox, observability |
| External AI models (right) | Provider-agnostic model API, called only from the default worker runtime |
| e6data stack and outbound delivery (bottom) | The e6data query engine runs SQL on Iceberg, Parquet and Delta tables in your object storage; Slack, email and ERP, MES or CMMS receive notifications, approvals and write-backs |

Your IP (workflows, skills, context and audit history) stays in the deployment's configured storage.

## Physical view

<Frame caption="AgentFlow physical view: processes, services and stores.">
  <img src="https://mintcdn.com/e6data/y-HeszjEBPpoJsYY/images/agentflow/physical-view.png?fit=max&auto=format&n=y-HeszjEBPpoJsYY&q=85&s=83232c614ba3aedeb3e392c634fdff54" alt="AgentFlow physical architecture. Kubernetes CronJobs and clients call the API gateway, which sends tasks through HAProxy to worker and sandbox pods; workers call AI models; pods use Redis and durable storage." width="2481" height="1791" data-path="images/agentflow/physical-view.png" />
</Frame>

The runtime is a Kubernetes-native deployment:

| Component | Role |
| - | - |
| API gateway (FastAPI) | Authentication, request routing, task submission, and SSE streaming back to clients. HTTP 8000, gRPC 50051. |
| HAProxy | Load-balances task submissions to worker pods. |
| Worker pods | Run the LangGraph agent runtime with an in-memory multiprocessing priority queue. Five lanes: chat and interactive SQL, trigger-started work, Python execution, semantic-layer and metric work, and indexing. No external task broker. |
| Sandbox pods | Isolate Python execution and validation. They get no application credentials, IRSA credentials or internal service token. Production images can add an `nsjail` boundary. |
| Kubernetes CronJobs | One per enabled cron trigger. Webhook and manual runs enter through the API. |
| Redis | Live coordination: task assignments, event streams, checkpoints, locks and caches. |
| Durable storage | Product state and history. S3-compatible storage or Azure Blob Storage; PostgreSQL for product storage in supported deployments. GCS is supported for telemetry delivery only. |

LLM calls come only from the default worker pods. The same picture deploys onto hosted infrastructure, your cloud or VPC, sovereign or private clouds, and on-premises Kubernetes. See [Deployment models](/agentflow/operate/deployment-models).


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