Loading image: AI Agents and Tool Use Explained - Verlin Labs AI article cover image…AI Agents and Tool Use Explained
An AI agent is an LLM wrapped in a loop: plan, call a tool, read the result, repeat. Tool use turns language models from text generators into systems that can search, calculate, and act - but only when boundaries, permissions, and verification are designed deliberately.
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From chatbot to agent
A standard chat completion maps input tokens to output tokens once. An agent runs multiple completions in a loop, often with structured tool calls between steps. The model proposes an action (search database, run SQL, send email); your runtime executes it and feeds results back as new context.
The mental model: the LLM is a planner and communicator; your code is the hands. Reliability lives in the orchestration layer - timeouts, retries, auth scopes, and human approval gates - not in the model alone.
- ReAct-style loops alternate reasoning text with tool invocations.
- Function calling APIs return JSON schemas the model must fill - validation is your job.
- Agents amplify both capability and failure modes when tools have side effects.
Designing safe tool boundaries
Each tool should do one thing, declare required permissions, and return structured output the model can parse. Never give an agent open-ended shell access in production without sandboxing. Prefer read-only tools first; add write tools only with idempotency keys and audit logs.
Prompt injection via retrieved web pages or user uploads is a real attack surface - treat untrusted content as hostile input that can hijack tool selection.
- Allowlist domains and API endpoints per agent role.
- Cap loop iterations and token spend to prevent runaway costs.
- Require human confirmation for irreversible actions (payments, deletes, external posts).
When agents help vs hurt
Agents shine on multi-step research, data gathering across systems, and workflows with clear success checks. They struggle when goals are vague, tools are flaky, or verification is impossible. A single-shot RAG query often beats a five-step agent for FAQ-style answers.
Start with the simplest architecture that passes evaluation, then add agent loops only where measured lift justifies complexity.
Key takeaway
Agents are LLMs in a supervised tool loop - design narrow tools, validate outputs, and cap autonomy. Reliability comes from orchestration and permissions, not a smarter model prompt alone.
