AI Agents: How Agentic Workflows Actually Work

AI Agents: How Agentic Workflows Actually Work

An AI agent is more than a chatbot. It is an LLM that can reason, plan, use tools, and act in a loop until a task is complete. Instead of generating a single response, an agent decides what to do next, executes it, observes the result, and repeats.

The Core Agent Loop

Every agent follows a variation of this loop:

User Task
   │
   ▼
┌─────────┐
│  Think  │ ◄──────────────────┐
│ (Plan)  │                     │
└────┬────┘                     │
     │                          │
     ▼                          │
┌─────────┐    Result      ┌────┴────┐
│   Act   │ ──────────────►│ Observe │
│ (Tool)  │                │ (Parse) │
└─────────┘                └─────────┘

1. Think — The LLM analyzes the current state and decides the next step
2. Act — It calls a tool (search, code execution, API call, file write)
3. Observe — It reads the tool's output and decides whether the task is done or needs another iteration

This is sometimes called the ReAct pattern (Reasoning + Acting).

What Makes Something "Agentic"?

Not every LLM application is agentic. Here's the spectrum:

The key differentiator is the LLM controls the flow. In a chain, you define the steps. In an agent, the model defines the steps.

Tool Use — The Hands of an Agent

Without tools, an agent can only think. Tools give it the ability to act on the real world:

┌────────────────────────────────────────┐
│              Agent LLM                 │
├────────────────────────────────────────┤
│  Tools Available:                      │
│  ┌──────────┐ ┌──────────┐ ┌────────┐ │
│  │ Web      │ │ Code     │ │ File   │ │
│  │ Search   │ │ Execute  │ │ System │ │
│  └──────────┘ └──────────┘ └────────┘ │
│  ┌──────────┐ ┌──────────┐ ┌────────┐ │
│  │ Database │ │ API      │ │ Shell  │ │
│  │ Query    │ │ Call     │ │ Command│ │
│  └──────────┘ └──────────┘ └────────┘ │
└────────────────────────────────────────┘

The LLM doesn't call tools directly — it outputs a structured request (usually JSON), and the agent framework executes it. This is called function calling or tool calling.

{
  "tool": "web_search",
  "arguments": {
    "query": "latest Node.js LTS version 2026"
  }
}

The framework runs the search, returns the results, and the agent continues reasoning.

Memory — Short-Term and Long-Term

Agents need memory to work effectively across long tasks:

Short-Term Memory (Context Window)

The conversation history and tool results within a single session. This is limited by the model's context window (128K–1M+ tokens in modern models).

Long-Term Memory (Persistent)

Information stored across sessions — user preferences, past decisions, learned patterns. Typically implemented with:

• Vector databases — Store and retrieve by semantic similarity
• Key-value stores — Store structured facts
• File-based notes — Markdown or JSON files the agent reads/writes

Working Memory (Scratchpad)

A place for the agent to think through intermediate steps, maintain a task list, or track progress. Some frameworks provide this as a dedicated "scratchpad" tool.

Planning Strategies

How an agent breaks down a complex task matters enormously:

1. Step-by-Step (Sequential)

The agent tackles one sub-task at a time. Simple but slow.

2. Plan-then-Execute

The agent creates a full plan upfront, then executes each step. Good for predictable tasks.

3. Adaptive Planning

The agent plans a few steps ahead, executes, then re-plans based on results. Best for tasks with uncertainty.

4. Tree of Thought

The agent explores multiple approaches in parallel and picks the best path. Expensive but powerful for hard problems.

Multi-Agent Patterns

For complex workflows, multiple specialized agents can collaborate:

Supervisor Pattern

One "manager" agent delegates tasks to specialist agents:

         ┌────────────┐
         │ Supervisor │
         └──────┬─────┘
        ┌───────┼───────┐
        ▼       ▼       ▼
   ┌────────┐ ┌────┐ ┌──────┐
   │Researcher│ │Coder│ │Tester│
   └────────┘ └────┘ └──────┘

Peer-to-Peer Pattern

Agents communicate directly with each other. Each agent has a specialty and can request help from others.

Pipeline Pattern

Agents are arranged in a sequence — output of one feeds into the next:

Research Agent → Writing Agent → Review Agent → Publish Agent

Common Agent Frameworks

Practical Considerations

When to Use Agents

• Tasks that require multiple steps with uncertain paths
• Tasks that need tool interaction (search, code, APIs)
• Complex research and synthesis

When NOT to Use Agents

• Simple Q&A — a single LLM call is faster and cheaper
• Deterministic workflows — a fixed chain is more reliable
• Latency-sensitive applications — agent loops add seconds per step

Reliability Tips

1. Set max iterations — Prevent infinite loops
2. Use structured outputs — Force the LLM to output valid JSON for tool calls
3. Add human-in-the-loop — Let users approve high-impact actions
4. Log everything — Every thought, tool call, and result for debugging
5. Evaluate systematically — Test agents on datasets, not just vibes

Conclusion

AI agents turn LLMs from passive text generators into active problem solvers. The key ingredients are a reasoning loop, tool access, memory, and good planning. Start simple with a single agent and a few tools, then scale to multi-agent patterns when the complexity demands it.

The best agent is the one that solves the task reliably — not the one with the most elaborate architecture.