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AI Agents Explained: Build One and Fix Common Issues

What Is an AI Agent?

An AI agent is software that uses a language model to work toward a goal, choose actions, and respond to the results. Unlike a basic chatbot that only returns text, an agent can call tools such as a search function, a database, or your application’s API. It may take several steps before it has enough information to answer or complete a task.

A useful agent does not need to be autonomous in every sense. A customer-support helper that looks up an order and drafts a response is an agent, even if a person approves the message before it is sent. The key pieces are a model, instructions, available tools, and a control loop that decides whether to act again or finish.

How to Build a Basic AI Agent

• Choose a narrow task. Start with a goal you can test, such as summarizing a support ticket and retrieving its order status. Avoid giving a first agent broad access to production systems.
• Define the model’s role. Write concise instructions describing the task, limits, and what to do when information is missing. Specify when the agent must ask a person instead of guessing.
• Expose a small set of tools. Give each tool a clear name, purpose, and typed inputs. For example, a lookup_order tool might accept an order ID and return status and delivery date. Validate inputs and enforce permissions in your application; model instructions are not a security boundary.
• Implement the action loop. Send the conversation and tool definitions to a model that supports tool calling. If the model requests a tool, your code validates and executes that request, adds the result to the conversation, and calls the model again. Stop when the model returns a final answer or a limit is reached.
• Test failures as well as success. Try missing IDs, malformed inputs, unavailable services, and ambiguous requests. Log tool calls and outcomes, set timeouts and a maximum number of steps, and require confirmation for consequential actions.

Keep secrets on your server, not in prompts or browser code. Treat tool output as untrusted input, and avoid letting retrieved text change the agent’s permissions. These controls make a prototype easier to debug and safer to expand.

Common AI Agent Problems

Why does the agent repeat itself or loop? The loop may be missing a reliable stop condition, or the tool result may not clearly indicate whether the task succeeded. Set a step limit, return structured tool results, and instruct the agent to explain when it cannot proceed.

Why is a tool call invalid? Check that the tool name and input schema match your implementation exactly. Validate arguments before execution and return a useful error the model can act on, without exposing stack traces or sensitive data.

Why does the agent make up results? It may be answering before a required lookup, or treating an empty tool response as success. Require evidence from the relevant tool for factual claims, represent “not found” explicitly, and test those cases.

Why is the agent slow or expensive? Each reasoning turn and tool round trip adds latency and model usage. Reduce unnecessary tools, keep context focused, cap steps, and compare models on your own evaluation set rather than choosing solely by reputation.

Choosing an API for Your Agent

Model access affects both implementation and operating cost. 59API is an AI API relay with pay-as-you-go access to models including Claude, GPT, Gemini, DeepSeek, Kimi, GLM, Qwen, and Grok. Its API base URL is https://api.59api.com, and it is compatible with Claude Code, Codex, Gemini CLI, and OpenAI SDKs. That can make it practical to try different models without building a separate integration for each provider. For example, compare Claude Sonnet 5.5 with DeepSeek V4 Flash on your agent’s real tool-use tests and budget.

59API offers low-cost access to native, official-quality models without model downgrades, plus a referral rebate. Review current pricing and model availability before deployment, since usage costs depend on the model and workload. Developers who want a pay-as-you-go way to experiment can sign up for 59API and test a small agent before scaling it.

What Should You Build First?

Build an agent that performs one bounded task, uses the fewest tools needed, and has a clear human handoff. Measure whether it completes the task correctly, how often it needs retries, and what each run costs. Those results will tell you whether to add capabilities, change models, or keep the system deliberately simple.

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