Most AI deployments wait for a prompt. Real operations do not. A manager defines a goal, delegates work, and people go collect facts, make decisions, and return results. What we need is AI agents that have an intent and initiate conversations with a goal.

The system below mirrors that familiar pattern with task templates, action chains, a watchdog that moves work forward, and scout agents that initiate conversations. It is simple to describe, strong in practice, and it scales. Everything described here is doable with existing technologies.

Let’s look at the main components that make up such a system:

System Components

1) Task template

A task template is the starting point. It contains an action chain plus basic metadata like name and description. Task Templates are executed by an event, on a schedule, when criteria are met, or on demand by a human. When a template is executed, it becomes a task instance with its own state and audit trail.

2) Action chain

An action chain is a list of actions that run in order inside a task. Each action has an embedded AI agent and the watchdog oversees execution. Actions come in a few types:

• Scout Agents. Initiates a conversation with one or more counterparties and collects structured data. Scouts are detached by design. People can ignore a message or take time to answer, so the system must tolerate latency and silence. Scout agents are implemented by temporarily replacing the normal support agent in the support chat with it self and initiating a conversation with a pre-defined templatable greeting.
• Create PDF. Uses a Word template and a simple JSON [key name]/[description] schema to generate a finished document as an artifact.
• Compose and send email. Writes an email from instructions and can attach artifacts created earlier in the chain.
• Perform other actions. Talk to an API, make a change in the database, post to a queue, or anything else needed. The sky is the limit here.

Every action in the task template and subsequently in the executed task instance includes:

• Instructions. Plain language guidance that becomes part of the system prompt. For example, write an email about subject X or collect data about subject Y.
• Input. The output of the previous action or fields fetched from the task data store.
• Output. A structured payload for the next action and for the task data store.
• Timeout policy. What to do if the action does not complete in time. Abort the task, alert a human, or ignore and continue.

The task has a general data store. Any action can write to it. Subsequent actions can read from it. This keeps the chain loosely coupled and easy to extend. LLMs are fantastically adopted to reading loosely structured data and understanding it’s context.

3) Watchdog

The watchdog advances the chain. It starts actions, monitors completion, enforces timeouts, and dispatches the next step. If an action stalls, the watchdog retries, switches channel, escalates to a human, or bails out per policy. It also keeps the log, timestamps each event, and stores artifacts. Think of it as the manager who checks progress and pushes the work forward.

4) Document templates

A document template is a Word file with {{tags}} and a schema for expected data. Templates can attach to a PDF action to produce a signed or branded document. They can also attach to a scout action so the scout knows exactly which fields to collect. The schema is the contract. It gives the system a way to validate outputs before side effects happen.

Three simple use cases

1) Collect pizza orders from the whole office

Let’s start off with something simple to illustrate the functionality of each component in the chain.

1. The task dispatches scouts to everyone on the team.
2. Each scout opens a chat, gathers order, size, and special notes, then writes the result into the task data store.
3. When all scouts complete or hit a timeout, the next action composes a single email to the pizzeria and sends it. Instructions for the final action that composes an email, has access to the task instance data store and an agent doing the job in the task template can be as simple as:

Write a pizza order to some,local@pizza.joint using the available requests in the data store, deliver the order to our office at Some Street 123 and invoice Acme Inc account number #123 or bring a card terminal. The role of the timeout and bail out functions here would be that if a scout fails to collect the pizza wishes from a team member, ignore it and send the order out at 11:30 anyway.

2) Help a customer file an insurance claim

Next let’s examine a different flow and another way to trigger an execution of a task instance.

1. In support chat, the customer tells the default agent that they need help filing an insurance claim.
2. The default agent selects the “insurance claim” task template and it is executed.
3. The template includes a Word document and a schema that lists incident fields.
4. A scout opens a conversation with the customer, collects the fields, and writes the JSON to the task data store and outputs it as the input to the next action.
5. A PDF action fills the Word template and outputs a final document.
6. An email action writes a short cover note and sends the PDF to the insurer mailbox.

Task templates automatically becoming tools that your customer default support AI agent can dispatch is the revolutionary part here.

How many different jobs performed by some human in the office are essentially: Go talk to someone about something, write a document about it and send it to someone? Think about it, quite a lot. Now just by defining the necessary task templates in an admin interface for each of these and selecting them to become available as tools lets you automate a lot of processes.

3) Update all customer phone numbers

1. The task dispatches scouts to every customer.
2. Each scout asks for the current phone number and returns a structured payload.
3. A database action validates the format and updates the record. Errors are logged for human review.

This example is powerful because the scout agents can all run in parallel. Updating the customer database can be done in 30 minutes for all customers compared to humans doing the work in a series. Timeout and bail out parameters here can be set to like a day and alerts generated for scouts that were not able to complete their tasks.

Why full platform control matters

Scout agents only work when you control the full system where the chain runs. You need to open and manage chat threads with a separate chat agent handling them, attach forms and files, tag each message with a correlation id, and capture consent. You need to validate outputs against a schema before taking any action. You need clean audit logs. Stitching this across many unrelated tools is brittle.

This is why platforms like Salesforce, Odoo or others are natural homes for detached chains. Both combine workflow, data, and communications. You can create records, run server actions, post to chat, and store artifacts in one place. You can also add human review steps that see the full context. If your organization cannot use these, you can build the ecosystem in house. The key is ownership of channels and data paths.

Chat control is critical. A scout that cannot open a thread, send a reminder, switch channel, or record consent will fail in real life. The watchdog needs reliable signals from the chat layer to decide whether to retry, escalate, or move on.

How this mirrors human work

This model behaves like a team. A manager defines a mission. Scouts go talk to people, collect facts, and return structured notes. Specialists draft a document or update a system. The watchdog is like a manager. It keeps time, nudges when needed, and escalates when something blocks. The result is predictable progress from intent to artifact without long back and forth.

The difference is that the system never forgets a field, always writes in the right format, and enforces time limits. It also produces an audit trail that shows who said what and when, what data was used, and which decision happened next. And all this can run in parallel and scale on demand.

Implementation checklist

1. Pick a platform where you can control chat, data, and actions in one place.
2. Define a task template with a short action chain that solves one narrow problem.
3. Write the scout instructions and the output schema. Keep it concrete.
4. Add a Word template with {{tags}} for any document you need to produce.
5. Implement timeout policies. Soft timeout for a nudge. Hard timeout for failover.
6. Validate every output against the schema before any side effect.
7. Log transcripts, artifacts, and decisions for audit and training.
8. Track a few simple metrics. Completion time, first try rate, timeout rate, and cost per completed task.

The power factor is the scout

The scout is what turns a static workflow into a living one. It is the agent that can initiate conversations and create new information by talking to people. Everything else is plumbing. Once you can send respectful, purpose built scouts that collect exactly the fields you need, the rest of the chain becomes routine. The watchdog moves the work along. The PDF has the right data. The database stays clean. Customers get what they need without long calls.

That is how detached agent chains become practical. They behave like people, run inside systems you control, and leave artifacts other systems can trust.

Author bio: Antti Kaipila is the founder of Nuuduu, a direct service platform that uses AI to automate service delivery.