<h2>What Are AI Agents?</h2>
<p>An AI agent is an LLM-powered system that can <strong>reason about tasks, make decisions, and take actions</strong> — including calling external tools, APIs, and databases. Unlike a simple chatbot that generates text, an agent operates in a loop: observe → think → act → observe the result → think again.</p>
<p>The difference between a chatbot and an agent is like the difference between a calculator and a programmer. The calculator gives you an answer; the programmer breaks down your problem, writes code, tests it, debugs failures, and iterates until it works.</p>
<h2>The ReAct Framework</h2>
<p>ReAct (Reasoning + Acting) is the foundational agent architecture. It interleaves reasoning traces with tool use actions in a loop:</p>
<pre><code>System: You are a research agent with access to these tools:
- search(query) → Returns web search results
- calculator(expression) → Returns calculation result
- lookup(term) → Returns encyclopedia entry
Use this format for each step:
Thought: [your reasoning about what to do next]
Action: [tool_name(arguments)]
Observation: [result from the tool - WAIT for this before continuing]
... (repeat Thought/Action/Observation until you have the answer)
Final Answer: [your complete answer]
Question: What is the GDP per capita of the country with the tallest building in the world?
The model would then reason through the steps: search for the tallest building → identify the country (UAE) → search for UAE GDP per capita → calculate if needed → provide the final answer.
<h2>Tool Use / Function Calling</h2>
<p>Modern APIs (OpenAI, Anthropic, Google) provide native function calling that's more reliable than text-based ReAct:</p>
<pre><code>const tools = [
{
type: "function",
function: {
name: "get_weather",
description: "Get current weather for a location",
parameters: {
type: "object",
properties: {
location: { type: "string", description: "City name" },
units: { type: "string", enum: ["celsius", "fahrenheit"] }
},
required: ["location"]
}
}
},
{
type: "function",
function: {
name: "search_products",
description: "Search the product catalogue",
parameters: {
type: "object",
properties: {
query: { type: "string" },
category: { type: "string" },
max_price: { type: "number" }
},
required: ["query"]
}
}
}
];
Key advantages over text-based tool use: structured JSON output, no parsing ambiguity, built-in validation, and parallel function calling support.
<h2>Agent Architectures Compared</h2>
<table>
<tr><th>Architecture</th><th>Complexity</th><th>Best For</th><th>Limitation</th></tr>
<tr><td><strong>Single-Turn Tool Use</strong></td><td>Low</td><td>Simple lookups, API calls</td><td>No iterative reasoning</td></tr>
<tr><td><strong>ReAct Loop</strong></td><td>Medium</td><td>Research, multi-step tasks</td><td>Can get stuck in loops</td></tr>
<tr><td><strong>Plan-and-Execute</strong></td><td>Medium</td><td>Complex workflows with clear steps</td><td>Plan may be wrong; costly to re-plan</td></tr>
<tr><td><strong>Multi-Agent</strong></td><td>High</td><td>Complex problems with specialised domains</td><td>Coordination overhead; harder to debug</td></tr>
<tr><td><strong>Reflexion</strong></td><td>High</td><td>Tasks requiring self-improvement</td><td>High token cost from reflection loops</td></tr>
</table>
<h2>Plan-and-Execute Architecture</h2>
<p>Instead of reasoning one step at a time, the agent creates a complete plan first, then executes it:</p>
<pre><code>System: You are a planning agent. Break the user's task into a numbered plan of
concrete steps. Each step should specify which tool to use and what inputs to provide.
Available tools: {tool_descriptions}
After creating the plan:
- Execute each step in order
- After each step, verify the result matches expectations
- If a step fails, revise the remaining plan
- When all steps are complete, synthesise the final answer
Task: {user_task}
Plan-and-Execute is better than ReAct for tasks with clear sequential dependencies — like data pipelines, deployment workflows, or multi-step form processing. ReAct is better for exploratory tasks where the next step depends on what you discover.
<h2>Multi-Agent Systems</h2>
<p>For complex problems, multiple specialised agents can collaborate. Common patterns:</p>
<h3>Supervisor Pattern</h3>
<pre><code>Supervisor Agent: Routes tasks to specialist agents
├── Research Agent: Searches and summarises information
├── Code Agent: Writes and reviews code
├── Analysis Agent: Processes data and creates visualisations
└── Writing Agent: Produces final documentation
<h3>Debate Pattern</h3>
<pre><code>Agent A (Defender): Argues for the proposed solution
Agent B (Critic): Identifies flaws and risks
Agent C (Judge): Evaluates both positions and decides
<h3>Pipeline Pattern</h3>
<pre><code>Agent 1 (Research) → Agent 2 (Analysis) → Agent 3 (Drafting) → Agent 4 (Review)</code></pre>
<h2>Building Reliable Agents</h2>
<p>Agents in production need safeguards that demonstrations and tutorials skip:</p>
<ul>
<li><strong>Maximum iterations</strong> — Always cap the agent loop (e.g., max 10 steps). Unbounded agents waste tokens and can loop forever</li>
<li><strong>Tool call validation</strong> — Validate tool inputs before execution. A hallucinated SQL query shouldn't hit your production database</li>
<li><strong>Fallback behaviour</strong> — When the agent can't solve a problem in N steps, escalate to a human rather than continuing</li>
<li><strong>Structured logging</strong> — Log every thought, action, and observation. Agent debugging without logs is nearly impossible</li>
<li><strong>Cost controls</strong> — Set per-request token budgets. A rogue agent can burn through your API quota in minutes</li>
<li><strong>Sandboxing</strong> — If the agent executes code, run it in a sandboxed environment (Docker, Firecracker, E2B)</li>
</ul>
<h2>Prompt Engineering for Agents</h2>
<p>Agent prompts require special considerations beyond standard prompt engineering:</p>
<ol>
<li><strong>Tool descriptions are prompts</strong> — The quality of your function descriptions directly impacts how well the agent uses them. Invest time here</li>
<li><strong>Few-shot tool use examples</strong> — Show the agent how to use tools correctly with 2-3 examples in the system prompt</li>
<li><strong>Error handling instructions</strong> — Tell the agent what to do when a tool returns an error. Otherwise it often retries the same failing call</li>
<li><strong>Grounding instructions</strong> — "Base your answer only on tool results, not your training data" prevents hallucination</li>
<li><strong>Completion criteria</strong> — Explicitly define when the agent should stop. "Stop when you have answered the user's question with verified data"</li>
</ol>
<h2>How AI Prompt Architect Helps</h2>
<p>Building agents starts with building excellent prompts. AI Prompt Architect's <strong>Generate</strong> workflow can scaffold agent system prompts with tool descriptions, ReAct formatting, and safety constraints built in. The <strong>Analyse</strong> workflow evaluates your agent prompts for common failure modes: missing tool descriptions, unbounded loops, and unclear completion criteria. Use it to go from a prototype agent to a production-grade one in minutes instead of days.</p>
This article was originally published with extended interactive STCO schemas on AI Prompt Architect.
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