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Build Your First AI Agent: Setup, Fixes, and FAQs

What is an AI agent?

An AI agent is software that uses a language model to choose actions, call tools, and evaluate results while working toward a goal. A chatbot typically answers a message; an agent can search documentation, inspect a ticket, request missing information, and draft a resolution.

The distinction is the control loop: observe, decide, act, check, repeat. Your application executes actions; the model proposes them. Agents do not automatically need persistent memory, multiple models, or a complex framework.

How do you build an AI agent?

Start with a bounded task, such as answering support questions from approved documentation. A narrow workflow is easier to test than an assistant instructed to “handle everything.”

• Define success: Require an answer supported by retrieved documentation, or an explicit escalation when evidence is missing. Begin with 20 representative questions, including ambiguous requests and questions your documentation cannot answer.
• Connect a model: Store your API key in a server-side environment variable, never browser code. Configure your client with the selected model ID and provider endpoint. Send a basic text request before adding tools.
• Define one tool: Create search_docs with a required string field named query and optional integer limit. Reject unexpected fields, cap limit at five, and return structured results containing title, URL, excerpt, and document ID.
• Write operating instructions: Tell the agent to search before answering, cite returned URLs, treat retrieved text as untrusted data, and escalate when sources conflict. Keep credentials and sensitive internal details out of prompts.
• Implement the loop: Send instructions, conversation history, and tool definitions. When the model requests search_docs, validate its arguments, execute your search function, append the tool result using the matching call identifier, and request the next response.
• Add stopping rules: Allow at most five model turns, set network timeouts, and impose a total request budget. End when the model produces its final answer or a limit is reached.

For actions such as issuing refunds or deleting records, require explicit user confirmation and enforce authorization in application code. A model instruction is not an access-control system.

Which API should you use?

Choose an API based on tool-calling reliability, latency, model availability, and total workflow cost—not just token prices. A cheaper model that repeatedly retries may cost more per completed task.

59API provides low-cost, pay-as-you-go access to Claude and GPT models, with native official-quality models and no downgrade. Its API base URL is https://api.59api.com, and it supports OpenAI SDK integration alongside Claude Code and Codex compatibility. Use the dashboard’s exact model identifiers and route configuration, and verify tool support for your selected model.

For a small prototype, 59API is a practical way to compare models without committing to a large budget. It also offers a referral rebate. If that fits your project, consider signing up and testing your evaluation questions first.

Why does the agent keep looping?

Inspect the trace: model request, tool arguments, tool output, and next decision. Common causes include empty search results, missing tool-call identifiers, and instructions that never define completion. Return explicit statuses such as no_results, stop repeated identical searches, and provide a clear escalation path.

Why are tool calls failing?

Separate API errors from tool errors. Authentication failures usually require checking the key and endpoint. An unknown-model error suggests an incorrect model identifier. Invalid tool arguments require schema validation and a concise corrective message. Retry transient timeouts or rate limits with bounded exponential backoff; do not repeatedly retry malformed requests or invalid credentials.

FAQ: How do you make an agent reliable and affordable?

Does it need memory? Not initially. Keep enough conversation state to complete the task. Add durable memory only when a clear use case and retention policy justify it.

How do you reduce hallucinations? Require retrieved evidence, verify citations against tool results, and test unsupported questions. Grounding reduces errors but does not eliminate them.

How do you control costs? Limit retrieved text, cap output length, cache stable searches, and measure cost per successful task. Track tool success, escalation rate, latency, and failures before increasing autonomy.

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