Build Agentic Search Features for Software With AWS SageMaker

Search has always been the silent backbone of digital experiences. Whether an engineer is looking for the right API call in product documentation or a customer is trying to find a winter jacket online, people expect search to deliver results instantly and intuitively.

Yet traditional keyword-driven systems often fail. They provide too many irrelevant options or fail when queries become conversational.

This is where Agentic Search takes the spotlight. Think of it as the difference between flipping through a dictionary and chatting with a subject-matter expert. An Agentic Search system doesn't just return results; it interprets intent and reasons through context and adapts them in real time. And thanks to AWS SageMaker, organizations can now embed this intelligence into their software without the burden of building an ML infrastructure from scratch.

What Is Agentic Search?

Agentic Search moves beyond keyword matches and introduces an agent-driven approach. Instead of treating "battery laptop" as two disconnected words, it recognizes that a user is looking for a laptop optimized for extended battery life. The system doesn't simply fetch text; it understands meaning.

Another hallmark is adaptability. Imagine you're searching a healthcare knowledge base for "cases where patients with asthma reacted to treatment X." A traditional search would flood you with documents containing those words. An Agentic system, however, reasons through the query, pulls relevant medical records, filters out noise, and presents the most contextually accurate information. That's the shift from "finding" to "understanding."

Why Agentic Search Is the Future?

Software companies are realizing that poor search experiences can have ripple effects. For customers, it means frustration and churn. For employees, it means wasted time digging through irrelevant information. In fact, research shows that employees spend nearly 20% of their workweek just looking for the correct information.

Agentic Search reverses this drain. By using embeddings and intelligent query agents, systems surface relevant and actionable results. Users ask questions naturally without forcing themselves to guess the "right keyword." Over time, the search becomes personalized, learning from individual preferences and behaviors.

It's not just a technical upgrade, it's a competitive advantage. Companies that adopt intelligent search report higher engagement and stronger loyalty, because users feel understood rather than filtered.

How AWS SageMaker Fits Into the Picture?

AWS SageMaker's ability to cover the entire ML lifecycle under one roof makes it essential in building Agentic Search. You don't just get a model; you get an ecosystem.

Instead of months of training models from scratch, SageMaker JumpStart gives teams pre-trained NLP models they can fine-tune within days. SageMaker Feature Store keeps track of embeddings and personalization data, ensuring the system has a consistent memory. And when it's time to scale, SageMaker Hosting deploys endpoints that can handle thousands of real-time queries.

But SageMaker isn't working in isolation. It often integrates with Amazon OpenSearch for vector retrieval, DynamoDB for user context, and Lambda functions to orchestrate agent logic. Together, they form a cohesive backbone that transforms an ordinary search bar into an intelligent assistant.

Architecture in Action

Picture the journey of a single search query. A product manager types: "How do I secure the new payment API with OAuth?"

1. The query first runs through a SageMaker endpoint, where it is converted into embeddings.
2. Those embeddings are compared against a vector index stored in Amazon OpenSearch, surfacing semantically similar documents.
3. A reasoning agent hosted in SageMaker interprets intent—recognizing that the user wants integration instructions, not just definitions.
4. The results are reranked, refined, and contextualized before being displayed.
5. Behind the scenes, the system updates the personalization engine based on the user's interaction, making the next search even sharper.

The outcome feels less like searching a library and more like conversing with a subject expert.

Case Study: Mactores' Success With an Internet Software Company

A US-based Internet Software company approached Mactores with a recurring pain point. Their developers were losing hours trying to navigate documentation for hundreds of APIs. The keyword-based system they had relied on returned pages of irrelevant results, leaving engineers frustrated and slowing down product releases.

The challenge wasn't simply one of scale; it was about intelligence. A query like "integrate the new payment API with user authentication" should have produced precise instructions. Instead, it returned scattered mentions of "payment,” "API," and "authentication" across unrelated documents.

The Transformation

Mactores deployed an Agentic Search framework using SageMaker and other AWS services. Here's how it unfolded:

• Using SageMaker JumpStart, we fine-tuned a transformer model trained on the client's technical documentation.
• Embeddings were stored and queried through Amazon OpenSearch, enabling semantic search.
• A SageMaker-hosted reasoning agent restructured user queries, identified intent, and reranked results.
• Powered by DynamoDB, personalization logic adapts searches based on developer roles and past interactions.
• SageMaker Pipelines automated retraining whenever new documentation was published to keep the system current.
  
  

The Outcomes

The results were immediate and measurable. Developers reported a 65% improvement in search relevance and a 40% reduction in the time it took to find critical documentation. Adoption rates doubled within three months, and the client accelerated release cycles while cutting operational costs by 30%.

Agentic Search became more than a utility for this client, it became a productivity multiplier.

Where Else Can Agentic Search Shine?

While the case study focused on developer documentation, the applications are endless. In e-commerce, it can help a customer find "hiking shoes that are waterproof but under $150." In healthcare, it can surface patient cohorts with remarkable precision. In large enterprises, it can transform knowledge bases into living assistants, guiding employees directly to what they need instead of burying them in irrelevant files.

Best Practices We Learned Along the Way

A few lessons stand out from deployments like this. Start small by fine-tuning pre-trained models instead of training from scratch; it saves both time and cost. Keep scalability in mind multi-model endpoints in SageMaker can make deployments far more efficient. Most importantly, close the loop with user feedback. Every click of the "did this help?" button is fuel for refining your agent.

And don't think of symbolic and neural methods as rivals. In many enterprise contexts, a hybrid approach delivers the most reliable results.

The Bigger Picture: Benefits of SageMaker

By now, the benefits of using SageMaker for Agentic Search are clear. Enterprises get a platform that scales effortlessly, integrates flexibly with the AWS ecosystem, and drastically shortens time-to-market. They also avoid the heavy operational burden of maintaining a custom ML stack.

For the Internet Software company in our case study, the result was simple: happier users, faster development, and lower costs. For other industries, the promise is the same.

Conclusion

The era of keyword-based search is fading. Users today demand systems that understand them, not just their words. Agentic Search, powered by AWS SageMaker, is the natural next step—delivering context, reasoning, and personalization in a way that transforms search into conversation.

At Mactores, we've seen firsthand how this shift unlocks real business value. By building Agentic Search with SageMaker, our clients have turned search from a bottleneck into a speed, satisfaction, and innovation catalyst.

If your organization is ready to elevate search from a basic utility to a strategic differentiator, now is the time to explore what SageMaker and Agentic Search can do together.

FAQs

• What is an agentic search feature?
  An agentic search feature uses AI agents that go beyond keyword matching. They understand user intent, context, and historical data to deliver highly accurate and personalized search results.
• Why use AWS SageMaker to build agentic search?
  AWS SageMaker provides a scalable environment for training, deploying, and optimizing machine learning models. Its integration with AWS services like Amazon Kendra, DynamoDB, and OpenSearch makes it ideal for powering agentic search features.

• How do agentic search features improve user experience in software?
  By analyzing queries contextually, agentic search reduces irrelevant results, improves response times, and personalizes outputs leading to better engagement and retention.
• How did Mactores help an internet software company with agentic search?
  Mactores used AWS SageMaker and supporting AWS services to build an AI-powered search system. This reduced query response latency by 40%, improved relevance scoring by 60%, and helped the company scale its user base without compromising search performance.