When to Build an Agent, Embed One in a Workflow, or Stick to Code

Every era gets its tools. The printing press. Electricity. The internet. Agents are next. But not every job needs one.

Agent Vs Workflow Vs Trad Code

What Is an Agent?

In the AI world, an agent is not a script or a chatbot. It’s not a productivity macro in fancy dress.

It’s an entity powered by a large language model (LLM) that can interpret, decide, and act—dynamically.

Given a goal, it can assess what tools are available, call them in sequence, and adapt if something breaks.

It doesn’t just run code; it reasons through a task.

LangChain defines agents as systems where the LLM is in control, choosing actions and navigating tools without hard-coded instructions. They're capable of exploration, decision-making, and course correction.

Think less flowchart, more improvisational jazz band with a lead musician that knows what it's doing.

But should every business task get this level of cognitive autonomy? Definitely not.

Let’s break it down.

When to Use a Full Agent

Use an autonomous agent when the task is:

• Complex and fuzzy – You don’t know ahead of time which tools or paths will be needed.

• Dynamic – Inputs shift, formats vary, and rules bend depending on the context.

• Open-ended – There’s no single “right” answer, only tradeoffs that require judgment.

Agents shine in environments with ambiguity and choice. Think intelligent assistants that process multi-modal documents, perform research across unstructured datasets, or route inquiries across business systems based on content, tone, and urgency.

The caveat?

These systems are harder to debug and more brittle if misapplied. Giving too much autonomy too soon is like handing the office intern a master key on day one.

When to Use an Agent Inside a Workflow

This is the sweet spot for most businesses right now.

A workflow with embedded agents allows you to orchestrate tasks where parts are predictable, but some decisions require judgment.

You define the skeleton, that is the core logic and data pathways, but hand off flexible steps to agents. The result is scalable, auditable automation with a brain at each junction.

Use this when:

• You want to automate without losing human-like decision-making.

• You have multiple systems/tools that need orchestration.

• You want to contain risks by controlling where the agent is in charge, and where it’s not.

Example: A KYC process where the initial form parsing is handled by a deterministic script, but document classification and fraud flagging are handled by an agent that interprets content and metadata in real time.

This middle path also allows you to evolve. As confidence in the agent’s capabilities grows, you can expand its control over the workflow.

When to Use Traditional Code

Sometimes, the best choice is the simplest.

Use conventional logic when:

• The rules don’t change.

• Inputs and outputs are clean and fixed.

• Accuracy, speed, and cost-efficiency are critical.

  
  

Tax calculations. Payment processing. API integrations that follow strict schemas. These are not places for reasoning or improvisation, they’re places for crisp logic, clear failure modes, and maximum performance.

Too often, businesses apply AI to problems that don't need it and this introduces latency, expense, and fragility where speed and precision are what matter.

Don't get us wrong we are very much PRO agent. Being pragmatic though and building the right solution we always put customer over code, and simplicity over complexity to get the result.

It’s about respecting the job.

Just use the right tool. Not the shiniest tool in the box.

Final Thought

Not everything needs to think. Some systems need to hum.

But for the systems that do require context, discretion, and scale there are enterprise grade agents already proving their worth. They aren’t for every task. But increasingly, they’re for the tasks that matter most.

And knowing when to build one, or when to stick to code, is going to be the defining skill of AI-era builders.