Prompt Engineering for AI Agents: From Guesswork to Precision

Summary: Good prompts aren’t about magic words. They’re about structured thinking and steering patterns in the right direction. In this article, Sascha Wolter, Principal AI Advocate & Advisor at NiCE Cognigy, shares four proven practices to optimize prompt engineering for AI Agent design, making interactions more precise, reliable, and effective.

A while ago, I asked my kids to “clean up a room.” You can probably imagine the result. Nothing happened.

So, I tried again: “Clean up your room now.” Better, but still open to interpretation. Finally, I spelled it out: “Put your toys in the box, books on the shelf, and clothes in the wardrobe before dinner.” Suddenly, the task was clear, actionable, and most importantly, it got done.

That’s when it struck me: writing AI prompts works exactly the same way.

Prompts Are Just Advanced Instructions

At its core, a prompt is nothing more than a request we give to a language model, whether in a web chat, a contact center, or an internal business application. And just like my kids with their room, AI will only deliver what you ask for, sometimes in unexpected ways.

Here’s the key: language models are trained to see patterns in a prompt and transform those into a completion. If you type “God save the…,” the model predicts the most likely completion of this pattern: King or Queen. Just as we instinctively connect red, green… blue or dog, cat… mouse, the AI continues patterns at a much larger and more complex scale.

This pattern recognition makes the technology powerful, but it also means that vague, contradictory, or incomplete prompts can lead to unexpected results. Your instructions become the anchor point that steers the model toward the pattern you want.

Tip 1: Treat AI Like a Mind Reader

The better you shape your request, the better the AI’s completion will be. That’s where prompt engineering comes in. Instead of vague input, you craft instructions that guide the model clearly toward the outcome you want.

The basics are simple:

• Be specific – vague prompts create vague results
• Provide context – the more the model knows, the better it performs
• Define the format – lists, summaries, bullet points, or narratives
• Set constraints – tone, style, or rules the output should follow
• Iterate – your first prompt is rarely your best

Just like refining my “room cleaning” command, iteration is often the key.

To make this process easier, I use a template that works in under five minutes:

A simple prompt template

Put the pieces together and drop this into ChatGPT or any LLM, and you’ll see how much sharper and more relevant the answers become.

To adapt this template for AI Agents and move from Prompt to Agentic behavior, the main change is in the last box: instead of ‘Command & Format,’ you define a ‘Greeting’ or ‘Starting Message.’ With this method, you can seamlessly bring AI Agents to life in tools such as Cognigy.AI.

Tip 2: Dynamic Prompting - Making AI Agents Truly Adaptive

Some time ago, I asked ChatGPT to suggest cycling routes. The answers? Generic, uninspired lists. Useful, yes, but not tailored (not to mention the lack of grounded data and the occasional hallucinations).

Then I made one simple change: I turned the prompt into something dynamic. Instead of sending the same static text, I pulled in variables: a city name, a sentiment, and even user profile data. This kind of manual tweaking of prompts is obvious, but it’s seldom done in a structured way (think of a prompt template). Once I applied it systematically, the agent suddenly suggested routes around Cologne. Then Düsseldorf. Then Graz. Each answer became context-aware, unique, and relevant.

That’s the power of dynamic prompting.

Every interaction with a language model is essentially one big prompt: A collection of messages that combine system instructions, context, tools, and conversation history. And you can programmatically change any of it, even mid-conversation. Think of it as giving your agent a flexible memory and personality, rather than locking it into a static script.

Incorporating variables for dynamic prompting in Cognigy.AI

With NiCE Cognigy, you can enrich AI Agents with dynamic content in many ways: From scripting to memory injection and grounding. In business scenarios, dynamic prompting is game-changing:

• Customer support AI agents that adjust tone based on user frustration levels.
• Digital advisors that automatically bring in external data (e.g. stock prices, live weather data, availability).
• Personalized virtual assistants that remember user information and preferences without re-asking every time.

Static prompts are like frozen snapshots. Dynamic prompts, on the other hand, are constantly adapting to context, user behavior, and external data. Once you see prompting not as “typing questions” but as programming context dynamically, you unlock the real potential of AI Agents.

Tip 3: Considering the Channel

In text-based interfaces, formatting is a strength. Markdown, bullet points, and headings help structure information and make it easier to scan. In voice channels, the same structure becomes the problem.

You don’t want your AI to say “asterisk, asterisk, B 104...”. Instead, you need prompts that instruct the model to avoid Markdown, avoid lists, and output in natural, flowing language. With prompt engineering, you can influence this:

• Tell the model explicitly never to use Markdown.
• Provide examples of how to pronounce abbreviations (e.g., “B104” should become “Bundesstraße 104” for Federal Highway 104).

However, those instructions are no guarantee. That’s where transforming the input and output might be handy. With NiCE Cognigy, the Endpoint Transformers enable you to modify both user inputs and Flow outputs based on the channel. Output Transformers, particularly, let you:

• Strip out Markdown syntax.
• Wrap text in SSML for improved pronunciation and prosody, ensuring listener-friendly responses.

These transformations are configured in the endpoint settings, giving you full control to post-process and refine AI outputs before they’re delivered.

Written channels demand structure. Spoken channels demand flow. By combining prompt engineering with Endpoint Transformers, you ensure your AI Agent does more than just answer: it communicates beautifully across any medium.

Tip 4: Mastering the Context

With newer models like GPT-4o and Google Gemini 2.0, context windows have expanded dramatically – and so has the model’s ability to understand that context. For instance, GPT-4o supports a 128k-token context window, enough to fit an entire Harry Potter novel.

Injecting knowledge into the AI Agent memory

In Cognigy.AI, you can leverage these context windows by simply placing relevant knowledge into fields like the short-term memory field to enrich LLM prompts and AI Agents. This content doesn’t have to be static – you can reference other sources, integrate transactional services, and inject dynamic content using CognigyScript (JavaScript).

Using the model’s context window is very effective, fast to implement, and a great starting point. With it, you can start simple, deliver value early, and evolve your architecture as needed. How you bring knowledge into context can always be adjusted later.

Of course, this approach has its own challenges. A larger context means more tokens, which can lead to higher latency and increased costs. When the required knowledge doesn't fit into the context or when greater control is needed, RAG comes into play. Even better, you can combine these two to get the best of both worlds.

The Key Takeaway

Good prompts are not about magic words. They are about structured thinking and steering patterns in the right direction. And the best part? You don’t need hours of training. 

Beyond a clear template, the aforementioned practices outline how to optimize prompt engineering in conversation design – swiftly and effectively. That said, prompt engineering is just one part of the equation. In a subsequent article, I will delve deeper into advanced tips for tool design to help prevent AI hallucinations. Stay tuned!