Smart AI Agents In the Wild

Remember when smart AI agents were mostly just a cool theory paper?  Well, those days are over.

In Part 1, we explored why the right infrastructure—especially databases built for more than just plain text—is foundational for building capable smart AI agents. Because let’s face it: if your agent can’t process images, video, PDFs or other multimodal data , it’s not exactly ready for the real world.

In Part 2, we cracked open the “brain” of an agent and showed how multimodal data fuels richer reasoning, contextual understanding, and better decision-making. TL;DR: Smart data leads to smart actions.

Now in Part 3, we go beyond architecture and design into actual deployment—Agents in the wild. These aren’t cherry-picked demos or academic one-offs, but real-world projects, built by teams pushing the boundaries of what smart AI agents can do with ApertureDB as a core part of the stack.

Let’s take a closer look at how teams are making it happen.
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🧠 Agentic RAG with SmolAgents: Smarter Search That Thinks for Itself

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Vanilla RAG has a fatal flaw: once it retrieves bad results, it’s game over. No retries, no corrections, no hope. Enter Agentic RAG—a smarter way to search. Built using Hugging Face’s SmolAgents and powered by ApertureDB, this project by community member Haziqa Sajid tackles academic paper overload.

By giving RAG a brain and the ability to reason, it brings structure and clarity to a sea of information.  Instead of relying on brittle keyword search, this smart AI agent refines queries, reruns searches, and even shifts strategies if results fall short.

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The Workflow

• Extracts information from complex, multimodal documents (like PDFs).
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• Allows agents to re-ask, refine, and retry when they hit a dead end.
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• Links semantic memory with structured metadata—crucial for good retrieval.
  
  

How The System Uses ApertureDB As The Brain the Brain Behind The Agent

• Store vector embeddings from academic PDFs
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• Enable multimodal retrieval (text, structure, even metadata)
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• Provide context for LLMs to reason more accurately

The Result

A research assistant that doesn’t hallucinate—it finds actual answers, complete with citations. Whether you are  looking for papers on Type II Diabetes or troubleshooting obscure medical treatments, this agent can crawl, adapt, and deliver.

This is not a trivial demo, but a production-grade, agent-powered research system. A template for anyone building smarter knowledge copilots, with a unified multimodal memory at its core. Exactly what ApertureDB was purpose built for.

📖 Read the blog

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🛋️ People Coming Over – The Personalized Agent That Shops So You Don’t Have To

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We have all been there: guests are coming over, your room looks sad, you are not alone. There is even a subreddit about it: r/malelivingspace.

But instead of browsing IKEA or hiring an interior decorator, you snap a photo and let your AI agent handle it.

This is People Coming Over, an image-to-action agent that analyzes your room and makes it better.

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The Workflow

1. Perceive: You upload a photo of your space.
   
   
2. Understand: The agent uses vision models to detect layout, objects, and overall vibe — clean, cluttered, cozy, chaotic?
   
   
3. Plan: It reasons about improvements: “Add a floor lamp here,” “Move the couch closer to the window,” or “Swap the art for something brighter.”
   
   
4. Act: It generates a shortlist of products and links to buy them, based on your preferences and budget.
   
   

Why ApertureDB?

Behind the scenes, this agent needs to store and correlate:

• Input photos
  
  
• Object detections and layouts
  
  
• Embeddings for visual similarity search
  
  
• LLM-generated suggestions and reasoning chains
  
  
• Product metadata and user preferences
  
  

That is a lot of heterogeneous, linked data, and it has to be queried in-memory, in real-time, as the agent reasons and responds. With ApertureDB: 

• Images, metadata, embeddings, and text are stored together in a single multimodal graph.
  
  
• The smart agent performs cross-modal search — e.g., “Find couches that look like this and match this vibe.”
  
  
• Latency stays low, because data is retrieved in milliseconds, not stitched together from S3, Redis, and Postgres or other third party tool configurations.
  
  

The Result

A multimodal shopping experience powered by ApertureDB. It is  like having a design expert, research assistant, and fulfillment coordinator rolled into one. But instead of juggling three apps and ten browser tabs, a single agent handles everything—from analyzing your space to recommending products and managing orders—at a fraction of the cost. No duct tape. No hacks. Just fast, intelligent decision-making—built on ApertureDB, the data foundation purpose-built for Agentic AI.

🔗 Explore the project

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💻 4. Metis AI – A Browser Agent That Automates SaaS Tasks

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SaaS tools are everywhere—but learning how to use them still feels like a chore. Metis AI aims to change that.
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The Workflow

• Scrape how-to docs, screen recordings, and video tutorials.
  
  
• Convert  them into multimodal instructions using ScribeAI.
  
  
• Store those instructions in ApertureDB as vectorized memory.
  
  
• Use  Gemini to match user intent to the right actions, then execute them.
  
  

Unlike other agents, Metis can perform complex, multi-step actions on SaaS platforms by grounding its understanding in prior examples.

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ApertureDB Plays A Critical Role In: 

• Storing and retrieving image+text-based guides for the web agent.
  
  
• Enhancing agent memory to complete tasks more accurately.
  
  
• Tracking agent performance using Weight & Basis Weave evaluator.
  
  

The Result

By leveraging ApertureDB, Metis’ AI team enhanced the performance and capabilities of a web agent by augmenting it with prior knowledge of the platform. Using tutorial videos, generated structured instructions for a multimodal agent, enabling it to:

• Execute multiple steps in response to a single customer query
  
  
• Incorporate prior context to improve task execution
  
  
• Track accuracy using an evaluator component integrated with W&B Weave
  

🔗See The Tech Behind Metis

ApertureDB Makes Agentic AI Work—At Scale
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What once felt like science fiction—agents that see, reason, and act—is now very real. Across these four projects, we have seen how developers are moving beyond prototypes and academic papers to deploy production-grade AI agents that actually do the work.

From automating SaaS workflows to decoding research papers, redesigning living rooms, and navigating complex interfaces like a human, one common thread runs through them all: ApertureDB.

It is the memory layer that enables agents to retain, retrieve, and reason across multimodal data— videos, images, text, structure, embeddings, and metadata—at scale and in real-time. No brittle pipelines. No fragmented stores. Just a unified, queryable system built for agents that need to think fast and act smarter.

The future of AI isn't just large models—It is intelligent agents with the power to reason, recall, and respond. ApertureDB is how you build them.

👉 Start your free trial of ApertureDB Cloud and give your agents the memory they deserve.

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