Building an LLM Agent? Start with the Right Questions.

LLMs (Large Language Models) are growing up. They're not just clever chatbots anymore; they're becoming intelligent agents, ready to automate tasks, lend a hand to your teams, and unlock serious value for your business.

The potential is truly exciting, imagine agents handling customer service, or helping your employees find answers in a flash. But hold on a second. Before you dive headfirst into building one, there's something crucial to do: ask the right questions.

You see, there's no magic formula for building an LLM agent. What works perfectly for one company might be a total flop for another. Think of it like building a house. You wouldn't just start buying bricks or windows. You start with a solid blueprint, right?

This guide isn't here to give you all the answers. Instead, it's here to help you ask the smart questions. These are the key things you need to think about before you even start designing and deploying your LLM agent.

1. Who are you creating this Agent for?

First things first: Who exactly will your LLM agent be talking to? The type of users your agent supports will shape everything else.

• Customer-facing agents talk to your users or clients. These need to be accurate, safe, and on-brand at all times. You need to:
  • Handle weird or unexpected questions well.
  • Avoid falling for trick prompts.
  • Speak in a consistent tone and voice.
  • Protect the company's reputation.
• Internal agents help your employees. These can be a bit more flexible and experimental. The focus here is on boosting productivity and access to internal knowledge, but you still need to think about data privacy and how to control what the agent can and can't do.

2. Should the Agent work alone or with human support?

Next, how much human touch does this agent need? Not every agent needs to work completely on its own. Often, the real magic happens when they partner with people.

• Fully automated agents handle tasks from start to finish. These work well for simple, repeatable jobs where the risk is low and speed matters most.
• Human-in-the-loop agents help humans make better decisions or do tasks faster, but the final call is made by a person. This is the best approach for anything sensitive, complex, or high-stakes.

3. How should the Agent respond to out-of-scope questions?

Let's be real: user questions aren't always neat and tidy. Your agent will face all sorts of unexpected stuff. How will it cope?

• Rules and logic are great for known questions with clear answers.
• LLMs shine when users go off script or ask vague or complex questions.
• The best agents usually mix both. Use rules where things are predictable and use LLMs where things get messy. But LLMs should include gaudrails and boundaries to ensure they handle out of scope situations gracefully.

4. Build vs Integrate: How custom should your Agent be?

Time for a big decision: Will your Agent be crafted from the ground up, or assembled using proven, off-the-shelf components?

Every part of an agentic AI pipeline — from LLMs and retrieval to tools, memory, OCR, and document parsing — involves choices:

• Pre-built tools Start fast by using platforms like OpenAI's GPTs, LangChain templates, or APIs for document parse (like Mistral OCR API or llamaparse). These get you to prototype or production quickly, with minimal setup and zero infrastructure.
• Building your own If you need tighter control, better privacy, or specialized functionality, consider self-hosting open-source LLMs (like LLaMA), building your own retrieval pipeline, fine-tuning for your domain, or deploying your own or opensource OCR models.

Many teams follow a hybrid path: Start lean using OpenAI GPTs + plug-and-play APIs → Validate the use case → Gradually swap components with custom or open-source alternatives for long-term value, cost control, and ownership.

Think of it not as one big decision, but as a series of choices across your pipeline — from inference to integration.

5. What will it cost, and What will you get back?

This isn't just about the upfront cost. You need to look at the whole picture: what will it truly cost over time, and what value will it actually bring back? Think Long term!

Costs to track:

• Development and integration
• API or infrastructure usage (especially LLM calls)
• Ongoing maintenance
• Data storage and compliance

Value to measure:

• Time saved per task
• Number of requests handled per hour or day
• Faster decisions or approvals
• Higher customer satisfaction
• Revenue from new use cases unlocked
• Future proof your business

Start with clear goals and KPIs so you can truly track what's working and what needs to change.

6. What data will the Agent use or process?

Alright, let's talk about data. This is a HUGE deal, and it absolutely has to be a top priority from day one.

Ask yourself:

• Will the agent handle sensitive information like personal data or financial info?
• What regulations apply (like GDPR, HIPAA, or CCPA)?
• How will the data be stored, protected, and deleted?
• Do you need to mask or anonymize the data?

Even for internal use, data risks are real. Make sure your team and your tools are ready.

7. How will you evaluate your Agent?

Building a smart agent is only half the job — evaluating its responses consistently is what makes it reliable at scale.

When choosing an evaluation method, balance speed, scalability, and judgment quality:

1. Exact Match

Pros: Fast, binary, easy to automate
Cons: Too rigid for open-ended responses
Best For: Fact-based Q&A, structured field extractions
Example: Checking if the agent returns "4" when asked, "What is 2 + 2?"

2. String Match

Pros: Simple keyword checks
Cons: Misses context, can't handle paraphrasing
Best For: Ensuring specific phrases or actions are present
Example: Verifying that a support response includes the phrase "reset your password."

3. Code-Based Checks

Pros: Ultra-fast, reliable, and scalable
Cons: Only works for well-defined rule-based outputs
Best For: JSON format validation, date math, field extractions
Example: Validating whether the output is a well-formed address object with fields like street, city, and zip.

4. Human Grading

Pros: High fidelity, can handle nuance
Cons: Slow, expensive, not scalable
Best For: Tone, empathy, subjective judgment, calibration sets
Example: A reviewer scores the agent's response to a customer complaint based on helpfulness and tone.

5. LLM-Based Grading

Pros: Fast, flexible, scalable for complex evaluations
Cons: Needs prompt tuning and reliability checks
Best For: Summarization, reasoning, tone/style evaluation
Example: Using an LLM to judge if a contract summary accurately captures the indemnity clause and uses professional language.

8. How will you monitor And Improve the Agent?

Remember, an LLM agent isn't a "set it and forget it" kind of thing. You'll need to keep it sharp and smart.

You need to:

• Track performance and flag errors.
• Update prompts and fine-tune behavior.
• Teach the model how your business works (via prompting or fine-tuning).
• Monitor feedback and make regular improvements.

Set up a clear feedback loop with both users and internal reviewers.

9. How will you drive adoption inside the Company?

You've built a fantastic agent – great! But that's only half the battle. Now, how do you make sure people actually use it?

To drive adoption:

• Pick real problems to solve, not just cool tech demos.
• Train employees on how to use it well.
• Ask teams to give feedback after using it.
• Set clear use cases with expected outcomes.
• Celebrate wins and share success stories.

You can even set policies like:

• All support teams must use the agent for draft replies.
• Product teams must include an "AI-supported" version for each spec.
• Everyone must submit an AI-generated first draft for standard documents.

The more it becomes part of daily work, the more value it will deliver.

10. What's the Long-Term Vision?

Finally, let's look way down the road. Today's LLM agents are just getting started.

Ask yourself:

• What tasks can be automated next?
• How can agents help teams collaborate better?
• Can agents act as knowledge hubs across departments?
• How do you make sure your AI tools evolve with your company?

Treat your LLM agent like a product, not a one-off project. Keep improving it, listening to users, and growing what it can do.

The Path Forward: No Magic Bullet, Just Smart Questions

There's no single magic recipe for building a successful LLM agent. It's complex, with many moving parts. But by asking these smart questions early in your planning, you'll dodge common pitfalls, save valuable time, and grab real opportunities.

Are you ready to design a powerful LLM agent strategy tailored for your unique business? Looking for a clear, customized roadmap and actionable steps, not just abstract ideas? We'd love to help. Let's talk ```