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title: Build Apps for Humans, AI Assistants, Autonomous Agents
description: A new series called AI Engineering Basics explores how the full-stack developer role is evolving as applications gain new consumers beyond human users: AI...
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> ## Documentation Index
> Fetch the complete documentation index at: https://daily.dev/llms.txt
> Use this file to discover all available pages before exploring further.

# Build Apps for Humans, AI Assistants, Autonomous Agents

**[Telerik](https://daily.dev/sources/telerik)** · 8 min read · 2 upvotes · 1 comments

## Summary

A new series called AI Engineering Basics explores how the full-stack developer role is evolving as applications gain new consumers beyond human users: AI assistants and autonomous agents. It distinguishes assistants (respond to individual requests, human stays in the loop) from agents (pursue goals across multiple steps with tool use and less human oversight), and introduces the concept of 'structured actions' - exposing app operations as callable actions with typed parameters and descriptions for models to use, referencing standards like Model Context Protocol. It maps existing full-stack skills (API design, database modeling, testing, monitoring, auth, UX) to their AI-era counterparts, arguing that becoming an 'AI Engineer' is an expansion of the full-stack role rather than a career change requiring ML research background.

## Full article

daily.dev links to this article rather than hosting it. Read it at the original source: <https://www.telerik.com/blogs/new-full-stack-developer-building-applications-humans-ai-assistants-autonomous-agents>

## Questions this post answers

### What is the difference between an AI assistant and an autonomous agent?

An AI assistant responds to individual human requests one exchange at a time, with a person staying in the loop for every meaningful step, like a docs chatbot or editor autocomplete. An autonomous agent is given a goal, plans steps, uses tools like APIs and databases, and adjusts until the task is done without a human reviewing each intermediate step, such as an agent that investigates a bug and opens a pull request.

_Developers weighing how much autonomy to build into a feature can track these design distinctions on daily.dev._

### What is a structured action in the context of AI agents?

A structured action is an operation that currently lives inside click handlers and form submissions, exposed instead as a clearly defined, callable action with a name, description, and typed parameters. The description matters as much as the types because it tells a model when the action applies and what its limits are, such as encoding that refunds over $500 require human approval.

_Teams designing agent-callable APIs can follow ongoing structured-action patterns on daily.dev._

## Community discussion

Top comments from developers on daily.dev.

**@agustinbarrientos** · 0 upvotes

> Action descriptions should include failure modes and approval boundaries, not just happy-path intent. I'd test them with adversarial parameter combinations before exposing the tool to an agent.

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---

Tags: [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#architecture](https://daily.dev/tags/architecture), [#mcp](https://daily.dev/tags/mcp)

[View this post on daily.dev](https://daily.dev/posts/build-apps-for-humans-ai-assistants-autonomous-agents-fawmgd6ly)

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