When to Use AI Agents

I see a lot of customers trying to build AI agents and unnecessarily complicating their lives. Let's unpack when it's appropriate to build one.

What's an Agent?

Let's first clarify what an AI Agent actually is. A good definition is a system that:

  • Takes goals or instructions from a human
  • Uses tools autonomously or semi-autonomously to achieve them
  • Adapts over time
  • Interacts with other systems (or people) to complete tasks

Basically, we've upgraded an LLM into something that can "figure it out" when given a problem.

When to Use an Agent

The Task Is Repetitive but Not Strictly Rule-Based

If a task requires judgment, prioritization, or pattern recognition (e.g., sorting customer inquiries, generating custom reports, or analyzing sentiment), an AI agent can outperform rigid automation tools. These are the sweet spots where human-in-the-loop or autonomous AI agents thrive.

Example: An AI support triage agent that classifies incoming tickets and routes them to the correct department.

You Need to Automate Multi-Step, Cross-Tool Workflows

If your workflow spans across multiple platforms or APIs (e.g., scraping data, cleaning it, generating insights, and emailing a report), and it requires decision-making at each step, an AI agent can be ideal.

Example: An AI research assistant that reads a set of documents, synthesizes insights, and creates summaries or recommendations.

The Task Requires Language, Logic, and Context

AI agents shine in tasks involving natural language understanding and reasoning. If your problem space involves interpreting instructions, working with unstructured data, or simulating decision-making, an AI agent might be more capable than traditional scripts.

Example: A contract reviewer agent that flags legal risks in NDAs using both language comprehension and rule-based checks.

Human Bandwidth Is a Bottleneck

If scaling a task requires hiring more humans, but the task is automatable with acceptable quality via AI with human oversight, then agents can bridge that gap, especially when quick iteration and scale matter.

Example: A marketing agent that generates and A/B tests ad copy variations based on performance data.

When Not to Use an Agent

The Task Is Simple and Rule-Based

If a task follows clear, deterministic rules (e.g., formatting phone numbers, syncing databases), traditional scripting or automation tools (like Zapier or Python scripts) are faster, cheaper, and more reliable.

Why not? Overkill. You don’t need an LLM to do what a few if-else statements can handle.

Accuracy Is Non-Negotiable

In domains like medicine, finance, or safety-critical systems, where mistakes can have high costs, AI agents can be too unpredictable and need to be rigorously validated.

Why not? Agents may hallucinate or act unexpectedly. These risks aren’t always acceptable.

You Don’t Have a Feedback Loop

Agentic systems learn best when they get feedback. If there’s no system of evaluation it’s hard to improve performance and trust the results.

Why not? Without oversight, agents don’t improve, and errors go unchecked.

Latency or Cost Constraints Are Tight

Agents typically orchestrate a series of LLM requests (e.g., planning, tool selection, execution, error-handling) and require supporting infrastructure (task queues, monitoring, retry logic, authentication, etc.). All of this adds both compute cost and end-to-end latency .

Why not? Too slow, too expensive for the ROI.

Final Thoughts

Before building or deploying an AI agent, ask:

  • Does this task involve complexity or ambiguity?
  • Would an agent actually save time or resources?
  • Can we measure success and give the agent feedback?
  • Do we trust the agent to make decisions - fully or partially?
  • Would a script, bot, or human do this better?

In the end, agents are tools and they’re best used intentionally, where their blend of autonomy and intelligence offers real leverage. Use them to amplify human work, not blindly replace it.

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