AI agents for developers: tools, loops, and guardrails
By TechlyUpUpdated 2 min readDevelopers
Quick answer
An AI agent is an LLM application that decides which actions to take — calling tools, reading results, and repeating — to complete a goal. Build agents with a small, well-described toolset, strict permissions, step and cost limits, human approval for consequential actions, and detailed logs. Start with a workflow where the steps are mostly known before giving a model open-ended control.
From function calling to agents
Function (tool) calling lets a model request a structured action — search, look up an order, create a draft. An agent wraps this in a loop: plan, call a tool, observe the result, decide the next step, and stop when the goal is met or a limit is reached.
Design the toolset carefully
Tools are the agent's capabilities and its risk surface.
- Few tools with clear names, descriptions, and typed parameters.
- Read-only tools first; write actions only when needed.
- Server-side validation of every tool call's arguments.
- Scoped credentials per tool, never broad admin access.
Guardrails
Limit steps, time, and spend per task. Require human approval for sending, deleting, purchasing, or changing access. Log every step with inputs and outputs so failures can be understood.
Workflows before autonomy
Many business problems are better solved with a fixed workflow that uses LLM steps than with a fully autonomous agent. Predictable workflows are easier to test and trust.
Workflow: fetch ticket → classify (LLM) → retrieve policy (search) → draft reply (LLM) → human approves → send. Agent: “Resolve this ticket” with tools for search, CRM lookup, and draft — more flexible, harder to test.
Common agent-building mistakes
Agents fail in ways simple LLM calls don't.
- Too many overlapping tools, so the model picks the wrong one.
- No step limit, leading to loops and runaway costs.
- Tools that accept free-form input without server-side validation.
- No logging, making failures impossible to diagnose.
Worked example: a research agent
A developer builds an agent to answer questions about internal documentation. It has two tools: search and read-document. The loop allows at most six steps, and the final answer must cite documents read.
Reviewing logs reveals the agent often searches repeatedly with similar queries. Improving the search tool's description and returning better snippets reduces wasted steps. The logs made the problem visible — without them, the team would only have seen slow, expensive answers.
Try it yourself
Build a two-tool agent (search docs, create draft) with a five-step limit and approval before any draft is saved. Log each step and review five runs.
Frequently asked questions
What's the difference between an agent and a chatbot?
A chatbot mainly responds with text; an agent decides on and executes actions through tools.
Which framework should I use for agents?
Start with your model provider's tool-calling API. Frameworks help with orchestration once you understand the basics.
What is MCP?
The Model Context Protocol is an open standard for connecting AI applications to tools and data sources in a consistent way.
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Sources and further reading
Examples are authored practice material, not measured learner outcomes. Tool behavior can change. Found an error? Contact TechlyUp with the page URL and correction.