Smarter Agents Start with Smarter Data

AI Agents, Agents, Agents! You have heard  the buzz, seen the demos, and maybe thought of building your own digital sidekick. But let's cut to the chase: what are these AI Agents, really? Are they sentient code? Tiny digital butlers? Close! 

Think of them as software that can perceive, decide, and act – like a programmable brain on a mission. They are built to tackle tasks autonomously, adapting to their environment and (hopefully) not causing a digital melt down. 

Imagine an assistant that doesn't just remind you about meetings but joins them, takes notes, and sends you a summary. Or a shopping buddy that finds  products, compares prices, reads reviews and orders the best deal. These are the kinds of things AI Agents are built for.

Under the hood, they are  powered by a cocktail of tech: LLMs for language, vision models for sight, and a whole toolbox of APIs. But like any good hero, they need a solid foundation, and that’s. where things get interesting…

To build “smarter” AI Agents — ones that can understand and interact with the world more like humans — we need to move beyond just text. After all, humans interpret the world through a variety of senses—sight, sound, touch, and more, integrating it all effortlessly. Similarly, AI Agents need to do the same. They need to understand and act on text, images, videos, audio, time series, and more — often together. That is  what makes them multimodal. But enabling that level of intelligence isn’t just about better models — it’s about better data infrastructure.

Multimodal Data: Power and Complexity

Multimodal data refers to data from multiple sources or formats — such as text, images, video, audio, time-series signals, embeddings and their associated metadata.  When building AI Agents, multimodal data is often processed together to understand context or make decisions.  

Sounds straightforward, but under the hood, it’s a mess:

• Heterogeneous data types require different storage systems and formats.
  
  
• Temporal and semantic alignment is critical — a frame in a video must be linked to the spoken word and metadata at the same moment.
  
  
• Semantic search must work across modalities: a query like “find video clips where someone’s tone is angry while pointing to a whiteboard” requires reasoning across audio, visual, and contextual signals.

And all of this must be accessible at scale, in real-time, and in-memory for modern agents to perform well.

The Legacy Stack: A House of Cards

Let’s be honest — most of us are still working with some version of this:

• Structured data in Postgres or MySQL
  
  
• JSON blobs in MongoDB
  
  
• Images, audio, documents, videos in cloud object stores or asset management systems.
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• Embeddings in a vector store (maybe)
  
  
• Time series in Influx or Prometheus
  
  
• Relationships tracked manually, in join tables, or not at all.

This stack works fine if you are serving dashboards or have minimal latency / throughput expectations. But try powering an AI agent that needs to juggle complex tasks using data stored this way, and things unravel fast.

The Challenges With Legacy Infrastructure

Siloed Systems, Siloed Context

Let's say your agent needs to answer a simple query:
"When did the delivery driver drop the package and what did they say?"

Now you’ve got:

• A video feed (in S3).
  
  
• Audio (separated out or embedded in video).
  
  
• Transcript (somewhere else).
  
  
• Metadata from a delivery app (probably Postgres)
  
  
• Timestamp alignment across them (good luck).

Correlating this mess is like trying to build a Lego castle with pieces from a million different sets.  It is slow, painful,and often impossible. Even if you have all the pieces, they are not connected. You are the glue — and that is code you did not want to write.

Even if you did spend painful cycles writing it, when you go to production, you hit security restrictions at every turn.

No Cross-Modal Querying

Want to find all the customer interactions where someone sounded angry, looked frustrated, and mentioned a specific product? With legacy systems, you’re essentially writing separate queries for each data type and then trying to stitch the results together.

Legacy databases don’t support multimodal joins either. You can’t just say:

“Give me images where the caption sentiment is negative and the object detected is a broken product.”

Try that on Postgres and it’ll throw a fit before quietly timing out. Even vector search systems—great for similarity—fall short when it comes to reasoning across text, visuals, and structure.

And while modern AI agents can abstract some of this complexity by calling tools, the burden does not  disappear. Data engineers are still stuck managing the underlying mess: keeping evolving data up to date, pipelines running, and systems in sync.

The result? Performance tanks, and maintenance becomes a constant uphill battle.

Real-Time? Not a Chance.

Smarter agents need fast, context-aware responses in realtime: 

• Retrieval-augmented generation (RAG) with both text and visuals.
  
  
• Scene understanding across video + audio.
  
  
• Sensor fusion from multiple modalities.
  

Legacy systems, however,  require multiple fetches, joins in app code, and pre-processing pipelines. You are paging from S3, parsing from JSON, calling five APIs… and by the time your agent responds, the moment has passed.

Aggressive caching might hide the pain temporarily, but  it is  not a sustainable strategy when your application needs to scale or deliver real-time performance.

In-Memory Reasoning Hits a Wall

Agents don’t just “look up” answers — they reason. Modern agents often do this in-memory, combining context from multiple modalities before making a decision.

Most legacy infrastructure:

• Can’t stream multimodal data into memory efficiently.
  
  
• Can’t align or cross-reference modalities quickly.
  
  
• Was never designed for embedding-heavy workloads or semantic relationships.

So you “Frankenstein” a solution in Python, stitch it together with NumPy, and then wonder why latency is spiking and your memory budget is gone. 

You Are Writing Too Much Glue Code

The more modalities you support, the more brittle your system becomes:

• Custom extract-transform-load (ETL) for each source.
  
  
• Metadata tracking in spreadsheets or ad hoc tables.
  
  
• Workarounds for time sync, data versioning, and corrupted files.

You did not become an AI engineer to build data plumbing. But here you are, knee-deep in pipelines instead of building features.

Legacy Infrastructure Is Killing Your AI Agent ROI

Legacy infrastructure can over time erode AI Agent performance, usability and ROI. Every slowdown, workaround, and system mismatch adds hidden costs, drains your budget, and stalls progress.

• Crippled Agents: Agents become shallow — they  can’t reason, connect modalities, or adapt to complex inputs.
  
  
• Dev Quicksand: You spend more time wrangling formats, glue code, and data hacks than actually building useful features.
  
  
• Lag City: Your users feel the pain — slow responses, clunky UX, and agents that stall when you need them most.
  
  
• Scaling Nightmares: What works in a demo falls apart at scale — your AI strategy stalls before it ever takes off.

Smart agents can’t thrive on broken foundations—It is  time to upgrade or get left behind.

So What Is The Alternative?

In Part 2 of our blog series, we will talk about what a modern solution looks like — purpose-built multimodal databases like AperttureDB that unify your data and make it easy for building smarter AI agents.

They handle:

• Real-time retrieval across modalities.
  
  
• Native embedding support enables semantic search, while integrated knowledge graphs enhance contextual relevance.
  
  
• Scalable performance without duct tape and patches.
  
  

In the final part of our series, we will bring it all together with real-world examples of how teams use ApettureDB to build AI Agents that work — and deliver serious ROI.

Stay tuned. If your agent is struggling to make sense of your multimodal data mess — you are not alone.

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