AI Agents Explained: What They Are and How to Build One
AI agents are the next leap beyond AI chatbots. Instead of answering questions, agents take actions — searching the web, reading files, calling APIs, writing code, and completing multi-step tasks autonomously. Here is what they are and how to build one.
The Precise Definition
An AI agent is a system in which a large language model can take actions in the world — not just generate text responses. The agent receives a goal, develops a plan to achieve it, executes steps (which may involve calling tools, searching the web, reading files, writing code, or calling APIs), evaluates the results, and continues until the goal is achieved.
The key difference from a standard AI chatbot:
| Dimension | AI Chatbot | AI Agent |
|---|---|---|
| Input | User message | User goal or task |
| Processing | Single model call | Multiple model calls + tool use |
| Output | Text response | Completed action or task result |
| Memory | Conversation history only | Persistent memory + external state |
| Autonomy | None — responds to prompts | Plans and executes steps independently |
| Tools | None by default | Web search, code execution, API calls, file I/O |
| Completion criteria | Each response is complete | Continues until goal is achieved or fails |
The Reasoning Loop
All agents follow a core loop — reason, act, observe, repeat.
Receive goal
The agent is given a high-level task: ‘Research the top 5 competitors for our product and summarise their pricing pages.’ This is a goal, not a prompt — the agent must determine how to accomplish it.
Plan the approach
The model reasons about what steps are needed: (1) identify who the top 5 competitors are, (2) find each company’s pricing page URL, (3) read each pricing page, (4) extract pricing tiers and key features, (5) write a comparative summary. This plan may be explicit (Chain of Thought) or implicit.
Execute a step using a tool
The agent calls the web search tool with a search query. It receives results. It evaluates whether the results answered the sub-question or whether another search is needed.
Observe and adapt
Based on the tool result, the agent decides: did this step succeed? What is the next step? Should the plan change given what was found? The model evaluates output at each step before proceeding.
Continue until complete
The agent continues the reason-act-observe loop until the goal is achieved, a stopping condition is reached, or a maximum number of steps is exceeded. It then presents the final result.
Practical Implementation Options
You do not need to build agents from scratch. These frameworks and approaches handle the plumbing.
OpenAI Assistants API
OpenAI’s managed agent framework. Create an assistant with a system prompt and a set of tools (file search, code interpreter, custom functions). The API handles the reasoning loop automatically. Best for developers who want a managed solution without building the loop manually.
LangChain / LangGraph
Python frameworks for building custom agents with fine-grained control over the reasoning loop, tool selection, and memory. Higher complexity than the Assistants API but full flexibility. Best for teams building bespoke agent workflows with specific requirements.
Make.com + AI (simple agents)
For simpler agent patterns — research, classify, then act — Make.com scenarios with multiple AI modules and conditional branching approximate agent behaviour without framework complexity. Best for no-code teams building task-specific automation that needs AI decision-making.
Bubble.io + backend workflows
For product teams building agent features in user-facing applications, Bubble.io’s recursive backend workflows can implement the reason-act-observe loop for specific, bounded tasks. Combine with OpenAI function calling for tool use.
What Is Worth Building Today
Research Agent
Give the agent a company name and ask it to produce a competitive intelligence brief: founding year, funding, products, pricing, key personnel, recent news, and positioning. The agent searches, reads pages, synthesises, and delivers a structured report. Replaces 2-3 hours of manual research per competitor.
Email Management Agent
Agent monitors an inbox, classifies incoming messages, drafts replies using relevant context from your CRM or knowledge base, flags items requiring human decision, and archives handled items. A structured, bounded version of email automation with AI judgment at each step.
Data Analysis Agent
Provide a dataset and a question. The agent writes code to analyse the data, executes it, evaluates the output, refines its approach if the first analysis was incomplete, and produces a plain-English summary of findings. Non-technical users get data analysis on demand.
Procurement Agent
Agent receives a procurement request, searches approved supplier catalogues, compares options against specification and budget, selects the best option, and raises a purchase order draft for human approval. Reduces procurement cycle from days to minutes for standard items.
What to Watch For
Current limitations
- Agents fail on tasks that require very long reasoning chains — they lose track after 10-15 steps
- Tool calling errors compound — an incorrect search result early in the chain propagates
- Cost scales with steps — a 15-step agent task costs significantly more than a single API call
- Agents can get stuck in loops when a step fails and they retry indefinitely
- Complex real-world tasks often hit unexpected edge cases the agent was not designed for
Essential guardrails
- Always set a maximum step count to prevent infinite loops
- Add human approval gates for consequential actions (sending emails, making purchases, updating records)
- Log every step and tool call for debugging and audit
- Start with narrow, bounded tasks where failure modes are predictable
- Test extensively with adversarial inputs before deploying in production
Want an AI Agent Built for a Specific Business Process?
SA Solutions builds task-specific AI agents — from research automation to document processing — using the right framework for your technical context and scale requirements.
