The AI Agent That Actually Ships

Your First AI Agent Is an MVP, Not Skynet

Open Twitter or YouTube and “AI agents” look like a countdown to Skynet: glossy demos of autonomous AGI CEOs, robot workers, and slide decks promising 10,000x productivity. In actual product roadmaps, though, agents look a lot more boring—and a lot more useful. They are narrow, task‑oriented bots that answer support tickets, clean up CRM data, or draft release notes on demand.

Most companies do not need a free‑roaming digital employee; they need one reliable workflow automated by 4 p.m. Friday. That means an agent that reads a support inbox, classifies messages, suggests replies, and hands them back to a human in Zendesk. Or a bot that turns raw meeting transcripts into structured Jira tickets with acceptance criteria and story points.

Moritz | AI Builder leans hard into that reality. His whole philosophy centers on shipping a working feature in hours, not architecting a hypothetical super‑agent that never leaves the whiteboard. In his “Let’s build a simple AI agent” video, the stack is pragmatic: a single LLM, a lightweight backend, and a UI that users can actually touch.

Rather than chase perfect autonomy, Moritz optimizes for tight feedback loops. You define one clear job—“qualify inbound leads and tag them by deal size”—wire in the tools (database, email API, maybe a vector search), and let the model handle the glue logic. If it fails, you tweak prompts and constraints and ship again the same afternoon.

Framed this way, the “simple AI agent” becomes the next step after macros, Zapier zaps, and Slack bots. It is still automation, just with a probabilistic brain that can interpret messy language and incomplete context. Instead of regexes and brittle if‑else trees, you get a goal‑driven system that plans a few steps ahead.

You can build and deploy that kind of agent in under a day using existing platforms. A small SaaS can add: - Automated onboarding email sequences - Tiered support triage - Personalized in‑app tips

Each one ships as a feature, not a moonshot, and starts delivering measurable value before the hype cycle moves on.

The Simple Loop That Powers Real Products

Most AI “agents” that actually ship run on a dead-simple loop: Goal → Plan → Tools → Execution → Feedback. A user states a goal in plain language, the system breaks it into steps, calls a few APIs, and iterates until it has something useful to return.

Under the hood, this looks less like sci‑fi robotics and more like a thermostat. You set a target state, the system acts, checks what happened, and adjusts. No academic multi-agent architecture, no exotic planning algorithms—just a control loop driven by a large language model.

At the center sits the Planner. The Planner is an LLM prompt that says, in effect: “Interpret the user’s goal, think step-by-step, and decide which tools to call.” For a sales-research agent, that might mean turning “find promising SaaS leads” into a 4-step plan: search LinkedIn, filter by headcount, pull domains, draft outreach.

Those steps flow into the Tool Executor, which handles the actual work. Tools can be: - REST APIs (CRM, Stripe, Notion) - Databases (Postgres, Supabase, vector stores) - Code runners (Python, JavaScript, shell)

The Executor takes structured tool calls from the LLM—often JSON—and runs them against real systems. It then feeds results back into the model as fresh context.

A lightweight Memory layer keeps the loop coherent across steps. Short-term memory tracks the current task state: which steps are done, what data came back, what failed. Long-term memory might live in a vector database, storing customer preferences, previous tickets, or past research so the agent can reuse work across sessions.

For many narrow use cases, teams can wire all of this into a single, well-structured LLM call. The prompt defines the Planner role, the available tools, the output format, and the stop conditions, while the application code simply enforces token limits and safety checks.

Frameworks like LangChain, LlamaIndex, or custom JSON “tool” schemas mostly formalize this loop. What ships to production is still the same pattern: a clear goal, a planning LLM, a handful of tools, and a feedback cycle tight enough to trust.

Forget Frameworks: The No-Code Agent Stack

Framework fever distracts a lot of indie hackers from a simpler truth: you can ship a working AI agent with three core pieces and almost no custom code. Moritz’s stack looks less like a research lab and more like a Lego bin: grab an LLM, a backend, and a UI, then wire them together with prompts and a few API keys.

At the center sits an LLM provider. Moritz leans on Claude because it writes clean code, handles long contexts, and stays controllable when you ask it to plan, call tools, and revise output. You treat Claude as the “brain,” then surround it with services that handle state, users, and interfaces.

For state and users, a service like Supabase does the heavy lifting. You get Postgres, row‑level security, and OAuth in minutes, not days. Instead of hand‑rolling auth flows, you let Supabase manage sessions while your agent reads and writes structured data like tasks, documents, or user preferences.

UI comes from prompt‑to‑app tools such as v0 or similar AI‑assisted builders. You describe the interface—chat window, history pane, settings toggles—and let the tool scaffold React or Next.js code. The agent becomes just another API endpoint your UI hits, not a monolithic “agent platform” you must swallow whole.

Airia, the product linked in Moritz’s video, slots into this stack as an orchestrator. It can manage prompts, workflows, and tool calls so you do not maintain brittle glue code yourself. Instead of coding a planner, router, and evaluator, you configure them in Airia and point your UI and Supabase backend at its API.

This tool‑centric approach beats heavyweight agent frameworks for one simple reason: time to first user. Full‑stack agent frameworks promise everything—memory, tools, routing, monitoring—but demand you learn new abstractions, config languages, and deployment stories before you ship anything. Indie hackers rarely have that luxury.

Composable tools also make debugging saner. If something breaks, you check: - Prompts and logs in your LLM provider - Database rows and auth rules in Supabase - Network calls and UI state in v0 or your front end

You can deepen the architecture later with ideas from A Practical Guide to Building Agents – OpenAI, but the first version should look like this: Claude for reasoning, Supabase for data and auth, v0 for UI, Airia for orchestration. Ship that, get feedback, then iterate.

The One-Page Doc That Controls Your Agent

Think of your agent’s “brain” as a one-page product spec. Not a vibe, not a personality, but a mini PRD that tells the model exactly what game it’s playing and how to win. Change that page and you often change the product more than swapping models or wiring new APIs.

A strong agent starts with a single, blunt role sentence: “You are a customer support triage agent for a SaaS analytics tool.” That line anchors every decision the model makes, from which tools it calls to when it should say “I don’t know.” Without it, the agent behaves like a chat toy, not a worker.

From there, you’re basically writing a compressed PRD. A simple but powerful template:

• Role: One sentence on what you are and who you serve
• Tools: Exact names, when to use each, and when not to
• Inputs: What the user will provide, with examples
• Success criteria: How you will be judged on each task
• Constraints: Hard rules, red lines, and guardrails
• Output format: JSON schema, markdown sections, or UI-ready text

Success criteria do the heavy lifting against hallucinations. “Only answer from the internal knowledge base; if the answer is missing, respond with `NEEDS_ESCALATION`” pushes the model to admit uncertainty instead of improvising. You’re trading open-ended creativity for predictable, auditable behavior.

Constraints act like bumpers in a bowling lane. Instructions such as “Never promise delivery dates” or “Do not modify user data without an explicit ‘CONFIRM’ step” prevent catastrophic but plausible-sounding actions. Models follow these rules surprisingly well when they’re short, specific, and near the top of the prompt.

Output format turns the agent from a chat partner into a component. If you say “Return a JSON object with `status`, `summary`, and `actions` fields, no extra text,” you can pipe that straight into a UI, database, or workflow engine. One line of prompt replaces dozens of lines of brittle parsing code.

For most real agents, that one-page spec is the highest-leverage artifact you create. A few clear sentences often outperform 500 lines of glue code and a weekend of debugging.

From Zero to Agent: A 30-Minute Workflow

Most people start with something boring and useful: a research assistant or a SaaS email helper. Think “summarize 5 articles on retrieval-augmented generation” or “draft a follow-up email for churn‑risk users.” Narrow scope keeps the agent predictable and shippable in under 30 minutes.

You begin by writing the one-page brief that doubles as the agent’s system prompt. Define the role (“You are a B2B SaaS sales assistant”), the tasks (summarize, prioritize, draft), and the output format (bulleted key points, 150-word email, neutral tone). This doc behaves like a mini-PRD that the model reads on every run.

Next comes tools. For a research agent, you wire in a web search API like SerpAPI or a native “browse” connector from a platform such as Airia. For a SaaS helper, you might connect a CRM or billing API so the agent can pull plan type, last login date, or ticket history before writing anything.

Configuration usually boils down to filling in a few fields, not editing YAML by hand. You paste your system prompt, drop in API keys for search or internal services, and toggle which tools the agent can call. Many builders expose this as a visual list of capabilities with checkboxes rather than code.

Once the brain and tools exist, you sketch a minimal UI. Moritz often uses a low-code front end where you drag in a text box for the goal, a “Run agent” button, and a scrollable panel for logs. If you need code, you ask AI to generate a React component that hits a single /run-agent endpoint.

When the user submits a goal—“Summarize these 3 URLs for a CTO”—the agent responds with its plan before doing anything. You might see: “1) Open each URL, 2) Extract key claims, 3) Compare approaches, 4) Output a 200-word summary plus 5 bullet recommendations.” That plan shows up live in the UI.

Execution happens step by step, with tool calls streamed back in real time. The agent fetches pages, parses content, maybe calls a secondary summarization model, then assembles the final answer. You watch each step as log lines: SEARCH, FETCH, PARSE, DRAFT.

Low-code platforms handle almost all of this orchestration for you. AI writes boilerplate API handlers, transforms JSON into clean text, and even suggests UI copy. Your “coding” often shrinks to approving or lightly editing the snippets that AI proposes.

The Backend That Builds Itself

Backend grunt work used to kill side projects before they shipped. Standing up a database, wiring auth, and exposing a few REST endpoints could eat a weekend, and that was before touching any AI logic. Now, commoditized backend-as-a-service platforms turn all of that into a 5‑minute setup step.

Supabase is the poster child here. You click “New project” and get Postgres, row‑level security, JWT‑based auth, and auto‑generated APIs, all hosted and monitored. For an AI agent, that means user accounts, session storage, and a durable memory layer arrive pre‑wired, not hand‑rolled.

On top of that, auto-backend generators are starting to behave like junior platform teams. Tools can read a prompt like “Create a /tasks API with CRUD for agent jobs” and scaffold: - Database tables - Type-safe client SDKs - Serverless functions - Basic monitoring hooks

Pair that with AI codegen and you get a loop where the model designs the schema, generates migrations, and writes the handler logic, while you just approve changes. Some platforms now push from prompt to live endpoint in under 60 seconds, no terminal required. The backend literally materializes around the agent’s needs.

For people following Moritz’s “build fast” philosophy, this flips the workload. You spend 80% of your time on agent logic—prompts, tools, evaluation loops, UX—and maybe 20% on wiring services together. The heavy lifting of auth, rate limiting, and data persistence lives inside managed services you barely touch.

If you want to understand how these pieces fit conceptually, the AI Agents for Beginners – Microsoft Learn Course maps out agents, tools, and backends in a structured way. From there, Supabase or similar services stop being scary infrastructure and start feeling like Lego bricks your agent can snap into on demand.

This Isn't a Toy. It's Your Next SaaS Feature.

Most AI agent demos stop at “look what it can do.” Moritz cares about “what will someone pay for?” His whole shtick is turning a weekend build into a $10,000–$20,000/month product, and agents are just another lever in that equation.

Hooking a simple agent into your existing app can instantly become a premium feature. A solo CRM can add an AI assistant that reads a customer’s history and drafts the next outreach email. A tiny analytics dashboard can ship a “Explain this spike” button that generates narrative reports for busy managers.

You do not need a sprawling multi-agent architecture to sell this. One tight loop—Goal → Plan → Tools → Feedback—can power a feature that looks like magic on the front end. Package it as: - “AI co-pilot” for your SaaS - “Automation workflow” that runs in the background - “Intelligent content generator” tuned to your customer’s data

Customers buy outcomes, not orchestration graphs. A marketing platform that auto-writes 5 on-brand campaign variants from last month’s winners can justify a higher tier instantly. A support inbox that suggests context-aware replies based on previous tickets reduces handle time and becomes an obvious upsell.

Moritz’s approach pushes you to think in revenue lines, not token counts. Can your agent save a sales rep 5 hours a week? That justifies a $49/month add-on. Can it replace a part-time VA for a niche agency? That’s a $199/month “AI ops” tier.

Smart indie builders wrap this into clear product stories. A course platform markets “AI that turns your lesson into quizzes, summaries, and social posts in 30 seconds.” A documentation tool ships “AI that reads your API and drafts guides for every new endpoint.”

The gap between a toy demo and a real feature is a thin layer of product thinking. Name the assistant, give it a button, tie it to a plan, and measure usage. Once customers see visible time saved or revenue generated, your “simple agent” stops being a novelty and becomes the reason they stay subscribed.

The Great Equalizer: AI Agents for Solo Founders

AI agents have quietly become the great equalizer in software. A solo founder with a laptop and a credit card can now orchestrate LLMs, APIs, and no‑code tools into something that looks suspiciously like a small engineering team. Moritz | AI Builder leans into that reality: you are not researching cognition, you are wiring up leverage.

Where a SaaS MVP used to demand 3–5 engineers, a designer, and a DevOps contractor, a single builder can now ship a feature‑complete product in days. Off‑the‑shelf components handle authentication, billing, vector search, and hosting. The “hard part” collapses into prompt design, UX choices, and picking the right tools.

Academic agent research still chases autonomous systems with long‑horizon planning, recursive reasoning, and multi‑agent simulations. Those projects burn through GPUs, PhDs, and months of tuning. Moritz’s approach instead treats an agent as a thin coordination layer over reliable services: a planner that calls APIs, not a robot butler that understands everything.

That contrast matters. Complex research agents often fail in messy, real‑world workflows because they optimize for benchmarks, not business outcomes. Tool‑driven agents, by comparison, ship as features: a support copilot inside a dashboard, a research runner for sales teams, a content generator wired into a CMS.

AI becomes raw leverage for solo founders when it automates the boring 80% of work. A single person can now delegate to agents that handle: - Data cleaning and enrichment - Customer email drafting - Documentation and changelog updates - Market and competitor research

Moritz’s channel revolves around this idea of AI as leverage. Every build shows the same pattern: automate the repetitive, error‑prone steps, then spend human time on product vision, positioning, and quality control. The agent does the grunt work; the founder decides what “good” looks like.

That shift changes who gets to participate in software. You no longer need deep React expertise or a backend architecture playbook to launch a $10,000‑per‑month product. You need a clear problem, a one‑page prompt that encodes it, and the willingness to wire AI into a feedback loop that actually ships.

What Comes After: From Task Agents to Builder Agents

Simple task agents like Moritz’s sit at the base of a rapidly stacking ecosystem. On one end you have chatbots glued to a few APIs; on the other, emerging multi-agent systems orchestrating dozens of tools, memory stores, and long-running jobs. Guides like The Agentic AI Handbook: A Beginner's Guide – freeCodeCamp map this spectrum from single-loop helpers to swarms of cooperating bots.

Agentic coding tools push this further. Editors like Cursor and GitHub Copilot Workspace no longer just suggest lines; they propose migrations, refactor entire directories, and run tests in the loop. A single prompt can trigger: “upgrade this app from Next.js 12 to 15,” followed by automated edits, dependency updates, and inline explanations.

Agentic coding changes who “owns” the codebase. Instead of micro-managing functions, developers set constraints, review diffs, and approve or reject high-level refactors. The agent becomes a semi-autonomous collaborator that understands project-wide patterns, not just the current file.

On the horizon, builder agents go beyond refactors and start from zero. Products like v0, Bolt.new, and Replit’s agent experiments already scaffold full stacks: React frontends, REST or tRPC APIs, database schemas, and auth flows from a paragraph of requirements. You get a runnable app skeleton in minutes, then iterate.

That unlocks a clear workflow for solo founders and small teams: - Use a builder agent to generate the UI, routing, and backend boilerplate - Hard-code the critical business logic and guardrails - Plug specialized task agents into specific workflows: support triage, billing ops, research, outbound email

Instead of building one mega-agent, you ship an app that hosts a constellation of narrow agents behind buttons, cron jobs, and webhooks. Builder agents handle scaffolding and structural changes; task agents handle repetitive, high-volume work. Humans stay in the loop as reviewers, not assembly-line coders.

Your Roadmap to Building a Real AI Product

Start small, ship fast, repeat. A real AI product usually starts as a single task agent that does one thing reliably: summarize a PDF, draft customer replies, clean up analytics reports. You don’t need a framework zoo; you need a clear goal, a composable stack, and a one-page prompt that reads like a mini-PRD.

Pick one painful workflow you touch every day. Turn it into a 30-minute agent: a research assistant that condenses three articles into a 5-bullet brief, or a support helper that turns tagged tickets into draft responses. Glue a chat UI to an API call, add a database if you must, and stop there.

Use a composable stack instead of a monolith: - An LLM provider (OpenAI, Anthropic, or a wrapper like Airia) - A no-code or low-code front end - A simple backend (serverless functions, Supabase, or an automation tool)

Your system prompt is your product spec. Write one page that defines role, tools, guardrails, and output format: “You are an assistant that summarizes URLs into a 150-word brief + 3 action items.” Treat every vague sentence as a future bug.

Build one agent this week, not someday. A realistic starter project: - A meeting-notes cleaner that turns raw transcripts into structured minutes - A newsletter summarizer that digests 5 links into a daily brief - A sales email drafter that turns CRM fields into first-contact emails

Shift your mindset from “learning AI” to “shipping AI features.” You learn more by debugging one misbehaving prompt than by watching 10 hours of theory. Ship a v0, watch it fail on real inputs, then refine the instructions and tools.

For deeper dives, go straight to primary sources. Start with OpenAI docs for API patterns, Microsoft Learn for Azure OpenAI and orchestration examples, and freeCodeCamp for hands-on tutorials. Use them as references, not prerequisites, while you ship your first agent into production.

Frequently Asked Questions

What is a simple AI agent in a practical sense?

It's a task-oriented system that takes a goal, breaks it into steps, uses tools like APIs or databases, and iterates with feedback until the task is complete. Think automation, not artificial general intelligence.

What tools do you need to build a basic AI agent?

A core stack includes an LLM (like Claude or OpenAI) for reasoning, a backend (like Supabase or serverless functions) for execution, and a clear system prompt to guide its behavior. Many no-code platforms can help wire these together.

Can I build an AI agent without being an expert coder?

Yes. The modern approach, championed by creators like Moritz | AI Builder, focuses on using low-code tools, AI-generated code, and pre-built services, making agent development accessible to beginners and product builders.

How are these simple agents used in real SaaS products?

They power high-value features like automated research assistants, intelligent email drafters, internal tools that query databases and generate reports, or lead qualification bots that interact with users.