Vector Stores: Brilliant for Similarity, Rubbish for Sequence
Common Mistakes in Using Vector Stores as Memory
Why do so many AI Agents struggle with memory? It’s not because they lack data—it’s because we’re using the wrong tools to store it.
Vector stores have become the go-to solution for AI Agents needing to retrieve context or find semantically similar data. But there’s a problem: Vector stores aren’t memory - at least, not the kind that tracks actions and sequences. They’re fantastic for similarity searches but downright useless when an agent needs to recall events in order.
If your AI needs to track exactly what happened, when it happened, and whether it's been done before, relying solely on a vector store will leave you frustrated.
Today, I talk you through the details and show you how I do it.
Vector Stores are Libraries, Not Diaries
Vector stores are powered by embeddings - mathematical representations of data that capture meaning - and excel at tasks like finding related documents or identifying patterns in unstructured knowledge.
As such, vector stores shine when it comes to retrieving related content - think of them as advanced search engines. They use embeddings to map meaning in a high-dimensional space, allowing agents to retrieve contextually similar information.
If you need an AI to find semantically related knowledge, vector stores are brilliant.
🔹 Where they work well:
Searching for documents that share a topic or theme.
Finding relevant conversations in a chatbot history.
Identifying patterns across unstructured data.
🔹 Where they fail miserably:
Keeping track of the exact sequence of events.
Avoiding action duplication.
Answering the question: “What did I do last time?”
This is where people go wrong.
Too many AI developers try to force vector stores into a role they’re not built for, leading to brittle systems that constantly forget what they’ve done - or worse, hallucinate false recall.
Why Vector Stores Break Down in Sequence Tracking
Vector databases, while powerful for similarity searches, face significant challenges in sequence tracking due to their inherent design limitations. Let's delve deeper into these issues:
1️⃣ Vector Search ≠ Ordered Recall
Vector databases excel at retrieving data based on similarity but lack mechanisms to maintain or recognise the order of data entries. This absence of temporal awareness means they can't inherently determine the sequence in which data points were added or events occurred.
📌 Example: Consider an AI agent managing a multi-step approval process. If tasked with retrieving steps based on content similarity, the agent might fetch steps out of their intended order. For instance, it could present the 'final approval' step before the 'initial review' step, leading to potential process disruptions and errors.
2️⃣ Lack of Deduplication Awareness
In vector databases, data is stored as high-dimensional vectors representing various attributes. While these vectors capture semantic information, the system might not recognise when different entries represent the same underlying data, especially if they have slight variations.
📌 Example: Imagine an AI-driven customer support system that logs support tickets. If a customer submits multiple tickets about the same issue phrased differently, the vector database might treat each ticket as a unique issue. Consequently, the AI could allocate resources to address each ticket separately, leading to redundant efforts and inefficiencies.
3️⃣ Over-Reliance on Similarity Leading to False Memories
Vector searches prioritise semantic similarity, which can sometimes result in retrieving data that is contextually alike but factually distinct. This can cause AI agents to recall and act upon incorrect or misleading information.
📌 Example: In a legal context, an AI system might retrieve case laws based on thematic similarities. However, without precise contextual understanding, it could conflate cases with similar themes but differing legal precedents, potentially leading to flawed legal interpretations or advice.
These challenges underscore the importance of integrating additional mechanisms into AI systems that rely on vector databases. Incorporating features like temporal tracking, deduplication protocols, and context-aware retrieval can enhance the reliability and accuracy of such systems.
Journals: The Unsung Hero of AI Memory
If vector stores are libraries, journals are diaries - they keep a strict chronological record of events. If an AI agent needs to track actions in order, a journal-based memory system is essential.
✅ Use a journal when your AI needs to:
Track step-by-step workflows.
Avoid repeating the same action.
Answer precise historical queries like “What was the last step completed?”
📌 Example:
A task automation agent logs completed tasks in a journal to prevent duplicate execution.
A compliance agent tracks changes over time to ensure an audit trail.
The Hybrid Approach: When to Use Both
The best approach isn’t either/or - it’s both.
Hybrid memory models combine vector stores for similarity-based retrieval and journals for ordered recall.
🔹 How it works:
Store ordered event logs in a journal.
Use vector stores for context-based lookups.
Query both when making decisions:
First, check the journal to see if the task was already completed.
Then, use vector search to find relevant knowledge.
This is exactly what I do in my own framework.
Below is a simple diagram of my “Hive Mind” data store system that my Templonix framework uses. I call it a Hive Mind because like the Borg, multiple agents can now use the same brain. This is actually the Version 2.0 of the framework - I’ve been refactoring a lot of it in recent weeks because I’ve changed the core of it capabilities - that’s another post by itself - coming soon.
As you can see, the Hive Mind is intended to essentially support four main capabilities:
A fast-access, short term memory tracking the immediate tasks to hand
A list of things the agent needs to work on
The ability to journal what it’s already done, and when it did it
To be able to recall skills and experiences (memories) from past actions
📌 Example: A very simplistic example of how this works really well is if I were to spin up a Templonix agent to spend the next 3 days conducting web based research for me on the best places in the UK for a Sunday roast dinner. It’d find them, write reports about them, and then continue until it’s “shift” finishes.
During this process, it’d journal what it’s found, meaning there’s no duplication in the content it writes. This makes the agent adaptive to the objective and allows for ever more granular searches to be executed to find what I want.
Over time, it’ll remember what it’s found, meaning when that if I want to chat to the agent about this subject through the Slack UI at a later date, I can ask it about the best Roast dinner in Lancashire, and it’ll tell me what it found by using it’s memory (the vector store).
The Pitfalls of Over-Reliance on Vector Stores
Based on what I’ve just described, it’s now easy to see one of the biggest mistakes in AI agent development is assuming vector stores are all you need. Without a proper structured memory system, agents become:
🚨 Forgetful: Can’t track past actions in sequence.
🚨 Repetitive: Redo tasks because they don’t know they’ve already been done.
🚨 Unreliable: Retrieve loosely related data instead of precise past events.
🔹 Fix it with a hybrid approach. Store event sequences separately and use structured queries to enforce recall precision.
Tools Matter More Than Hype
The issue isn’t vector stores - it’s how we use them.
The AI world is full of over-promised, under-delivering “solutions” that are more hype than substance. AI isn’t magic - it’s engineering.
The real lesson here? The best AI isn’t the one with the biggest memory—it’s the one that uses memory the right way.
As I always say, first principles first. Pick the right tool for the job, and suddenly your AI Agent will stop tripping over its own past.
Actionable Takeaways
So what’s the learning from all this?
📌 First off, ditch the idea that vector stores are some kind of all-seeing memory vault - they’re not. They’re great for finding related info, but useless when it comes to actually remembering what happened, in what order, and whether it’s been done before.
The answer? Use a hybrid memory model.
That means pairing vector search with structured journaling so your AI isn’t just pulling related fluff, but actually keeping track of its own history. Think of it like this: vector stores are your AI’s Google, while journals are its diary. Without both, it’s either guessing or forgetting.
📅 Next, stay on top of memory hygiene. Your AI doesn’t need to hoard every interaction like some digital pack rat. Instead, summarise, condense, and update. Nobody needs a memory dump every five minutes - just the critical stuff that keeps the AI functioning smartly. Keep it lean, keep it useful, and make sure old junk gets flushed so the agent isn’t sifting through nonsense.
⚡ Finally, make retrieval context-aware. AI Agents aren’t mind readers - yet. If they don’t understand the context of what they’re fetching, you’ll get irrelevant or downright wrong answers.
The solution? Design retrieval mechanisms that actually align with the task at hand.
Your AI should be smart enough to pull what’s useful and ignore what’s not, rather than just throwing back whatever looks vaguely similar.
Until the next one, Chris.
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