If you strip away the hype, an AI agent is a simple loop that asks a model what to do, uses tools when needed, and repeats until the goal is met. That’s it. No mysticism—just a clean control flow that lets an LLM plan step‑by‑step and act through tools.
On this page you’ll find a minimal code skeleton, a step‑by‑step explanation, when to add bells and whistles, and how to ship agents to production with Draft’n run—our observability‑first platform for agentic AI and the visual AI workflow builder.
The entire agent, in a few lines
INSTRUCTIONS = "Use tools at your disposal to complete the task"
TOOLS = [web_search, knowledge_access]
def ai_agent(task):
while not finished(task):
answer = ask_llm(INSTRUCTIONS, TOOLS, task)
if has_tools(answer):
return ai_agent(run_tools(answer))
else:
return answerThis is intentionally minimal.
What this logic does (step by step)
- You give a goal and a toolbox.
INSTRUCTIONSexplains the objective and encourages tool use.TOOLSlists capabilities (search, DB, CRM, email, etc.). - Ask the LLM for the next step.
ask_llm(...)returns either a direct answer or a plan that includes tool calls (e.g., “search for X”, “query table Y”). - If tools are needed, execute them and loop.
run_tools(answer)performs the calls. The fresh results become the new task—you recurse (or iterate) so the model can refine its plan with the new evidence. - If no tools are needed (or the goal is satisfied), stop.
finished(task)defines success (e.g., “we have three sources,” “we drafted the email,” “the SQL returned rows”).
That’s the classic perceive → decide → act → repeat cycle.
“Is this really an agent?”
Yes—because it:
- pursues a goal,
- perceives context (via tool results),
- decides the next move (via the LLM),
- acts through tools, and
- iterates until the goal is achieved.
No extra ceremony is required for agency. The agenty‑ness comes from the closed loop between reasoning and action.
Why this is enough (most of the time)
- Planning emerges each step: The LLM proposes the plan turn‑by‑turn; you don’t need a heavyweight planner upfront.
- Tools provide leverage: Web search, vector databases, CRMs, spreadsheets, and APIs extend the model’s reach.
- Iteration adds working memory: Each pass incorporates new facts and narrows uncertainty.
- A clear
finished()keeps scope tight: You know when to stop—shippable by default.
This pattern gets you surprisingly far in real products: research copilots, report builders, sales assistants, internal knowledge helpers. Explore examples across AI automation and our case studies.
Where Draft’n run fits
Shipping agents is less about prompts and more about reliability, safety, and visibility. Draft’n run gives you:
- Visual workflows with typed inputs/outputs in the AI workflow builder
- Observability by default (traces, metrics, costs) across every step—see agentic AI
- Safe tool use with schema‑validated arguments and deterministic connectors—see integration options
- Knowledge features for retrieval‑augmented agents and document automations—see custom AI
Ready to see it live? Request a demo or check pricing to get started.
When to add more (and what to add)
Start with the tiny loop; extend only when the pain is real:
- Reliability: retries, backoff, structured outputs (JSON schema), unit tests on prompts and tools.
- Safety & cost: limits per step, budget accounting, red‑teaming checks.
- Observability: trace logs, step transcripts, tool I/O capture.
- Memory: summaries or embeddings keyed by user/session/project.
- Parallelism & events: background jobs, webhooks, schedulers for long‑running tasks.
- Domain success tests: a robust
finished()that encodes what “good” means in your context.
Practical tips for shipping
- Constrain the toolbox. Fewer, well‑designed tools > many vague ones.
- Make tool contracts explicit. Names, arguments, return types. Fail fast on bad calls.
- Keep prompts stable and short. Put policies in
INSTRUCTIONS; pass the task separately. - Log everything. Steps, tool calls, inputs/outputs. Your future self will thank you.
- Define success upfront. A crisp
finished()prevents infinite dithering.
Mini‑FAQ
Q: Which model should I use?
A: Any model, but you should look at the “instruction following” benchmarks if you want to build an agent. For instance the LM arena benchmark is a good starting point.
Q: How many steps should I allow?
A: Keep it small (3–8) until you see real tasks that require more depth. Depth inflates cost and risk.
Q: How do I avoid hallucinated tool calls?
A: Require structured outputs (e.g., JSON with tool_name and args), validate against a schema, and reject/reprompt on mismatch.
Takeaway
If you understand this five‑line loop, you understand AI agents. Everything beyond it—memory, schedulers, critiques, graphs—is polish you add for specific reasons, not the essence. Start here, ship sooner, and only grow the complexity when your use case truly demands it. When you’re ready, build and observe your agent with Draft’n run’s AI workflow builder or talk to us via the demo page.
