When AI Agents Need More Than a Quick Answer

Most AI agents handle short tasks well. Submit a prompt, the agent calls a few tools, and you get a response within seconds. That model works fine for summarizing a document or pulling information from the web. The problem appears the moment a task stretches beyond a single response cycle.

Consider something like: go through the last three days of my emails, draft replies for anything urgent, and create Linear tickets for the engineering-related ones. That is not a seconds-long operation. It could take minutes, or hours. If the server crashes or the process restarts at any point during that work, everything is lost. No retry logic saves you. No resume is possible. The task starts over from zero.

That is the exact gap this study guide addresses.

Understanding the Architecture Before Writing a Line of Code

The system described in this guide splits into two distinct planes. A control plane handles user-facing interactions - in this implementation, a Next.js frontend. An execution plane handles the actual agent work. These two planes never call each other directly, and that separation is deliberate.

When a user submits a goal, the gateway first runs a pre-flight check to verify that the required Composio tool connections are active for that user. If the connections are live, the gateway hands the task off to Temporal and returns immediately. The user does not wait for a result. The task runs entirely in the background. This is not a chat application - the user dispatches the task and moves on.

From there, Temporal places the task on a queue, and a worker pod picks it up. The worker runs the agent loop: the LLM reasons over the goal, Composio executes the tools, and the result is written back to Temporal. The frontend polls the gateway for status updates, so the user can see progress without any manual action.

What You Are Actually Learning to Build

This guide covers six distinct skill areas, and understanding them separately makes the full system easier to absorb.

The agent loop is where Claude and Composio interact. Claude reasons over the goal and decides which tools to call. Composio executes those tools against real external services. The loop continues until the task is complete or the LLM determines it cannot proceed further.

Temporal provides durable execution. If a worker crashes mid-task, Temporal does not lose the state. The workflow resumes from where it stopped. This is the core mechanism that separates a toy agent from one that can handle real workloads.

The gateway decouples task dispatch from execution. Agent tasks can take minutes or hours - placing that logic inside an API layer would make the control plane slow and fragile. The gateway stays fast because it only dispatches; it never runs agent code.

Docker containerization wraps both the worker and the gateway into portable images that Kubernetes can manage and scale independently.

Kubernetes deployment runs the full system on a local cluster. The guide covers deploying the complete stack locally before any production considerations arise.

KEDA autoscaling watches Temporal queue depth and scales worker pods based on actual pending work. When the queue is empty, workers scale down to zero. When tasks arrive, pods spin back up. No idle compute, no manual scaling decisions.

The Pre-Flight Check and Why It Matters

One detail that deserves specific attention in any study of this system is the pre-flight check at the gateway level.

Before handing a task to Temporal, the gateway confirms that the required Composio tool connections are active for the requesting user. This check exists because the application also supports Linux CronJob-style scheduled tasks - no human is present at dispatch time to notice a failure. Failing fast at the gateway is far less expensive than allowing a broken workflow to consume queue space, spin up a worker pod, and then fail deep inside the execution plane with no clean recovery path.

This pattern - validate preconditions before committing to a long-running operation - appears across distributed systems and is worth understanding as a general principle, not just a feature of this specific implementation.

The KEDA Scaling Model and What Makes It Practical

KEDA stands for Kubernetes Event-Driven Autoscaling. In this system, it watches Temporal’s queue depth as its scaling metric. The relationship is direct: more pending tasks means more worker pods, an empty queue means zero worker pods.

Scaling to zero is significant. In most Kubernetes deployments, services maintain a minimum of one replica at all times to avoid cold-start latency. KEDA allows the worker tier to drop completely to zero, which matters when tasks arrive in bursts or when the system sits idle for extended periods. The cost profile changes accordingly.

The moment a task enters the Temporal queue, KEDA detects the queue depth change and begins scaling workers up. The worker that starts picks up the task, runs the agent loop, and writes the result back to Temporal. If additional tasks are queued, additional pods are available to handle them in parallel.

The Six Topics This Guide Covers, in Sequence

For study purposes, the guide is organized into these sections: What the plan is (the architecture), dispatching a task, running the task, scaling, project setup, core application components, the agent loop, making execution durable with Temporal, the agent gateway, containerizing the application, deploying to Kubernetes, autoscaling with KEDA, and a live demonstration.

Reading the architecture section before touching any code is time well spent. The separation between control plane and execution plane is not obvious from the component list alone - it only makes sense once you understand why agent tasks cannot live in the API layer.

The containerization section covers both the worker and the gateway as separate Docker images. This matters because they scale independently: the gateway handles HTTP traffic and stays up continuously, while workers scale to zero between tasks.

Tools Involved and Their Roles

Temporal manages durable workflow execution and the task queue. It is the reason a crashed worker does not mean a lost task.

Composio provides the agentic tool layer. It connects the LLM to external services - email, project management tools, and others - and handles the actual execution of tool calls.

KEDA reads queue depth from Temporal and translates that into Kubernetes scaling decisions.

Kubernetes runs the worker pods and the gateway, manages container lifecycle, and provides the infrastructure layer the whole system depends on.

Claude is the LLM doing the reasoning. It receives the goal, decides which tools to invoke, processes results, and determines when the task is finished.

Next.js provides the frontend. It polls the gateway for status and displays progress to the user.

What Makes This Architecture Worth Studying

The architectural constraint that stands out most clearly here is the strict separation between dispatch and execution. The gateway exists only to receive tasks, run the pre-flight check, and hand off to Temporal. It never touches agent code. The execution plane runs independently, driven entirely by queue depth.

That constraint is what allows the control plane to stay responsive regardless of what is happening in the background. A three-hour email triage task running on a worker pod has zero effect on the gateway’s ability to accept new requests.

The application also supports scheduled tasks through a Linux CronJob-style mechanism - no human involved at dispatch time. The same pre-flight check that protects interactive users also protects scheduled runs from silently failing inside the queue.

KEDA’s scale-to-zero behavior means a fully idle system consumes no worker compute at all.