Today we are entering the era of context engineering, and this will probably become the most important discipline in AI-powered software.
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Exactly — and the "orchestration" framing is underrated. Most people try to solve the memory problem by throwing more context at the model, but the real work is upstream: deciding what deserves to persist, what should decay, and what should never have been stored at all.
I find the hardest part is not the architecture, it's the epistemics: you need the system to know when its memory is wrong or stale. A confidently stored wrong fact is worse than no memory at all.
Thanks Vic! and sorry for the late reply.
Yes, you’re absolutely right that the real challenge is epistemic instead of architectural. As mentioned, the dangerous thing is not lack of memory, but unchallenged memory.
Your point about orchestration is also key...In the end the hard part is not just storing context but deciding what deserves to persist and what should leave...That’s fundamentally is judging, not a storage problem. Basically, context engineering is less about giving models more memory and more about giving systems the ability to remain self correcting over time.
Really thoughtful comment, thanks again 🙏🏻
The 'self-correcting' frame is more precise than 'self-aware' -- because it implies a feedback loop rather than just accumulation. The goal isn't an agent that remembers everything. It's an agent that updates correctly when its model of the world is wrong.
Most context architectures optimize for retrieval (how do we surface the right memory at the right moment?). The harder design problem is expiry -- knowing when to let a prior conclusion become stale. A fact about a company's capital structure from Q1 may be actively wrong by Q3. A retrieval system that returns it confidently is worse than one that returns nothing.
The self-correcting property you're describing requires explicit belief revision, not just lookup. That's the much harder problem -- and probably why most production systems quietly ignore it.
This nails it. I've been building AI agent systems and the "retrieval is not memory" framing is exactly right. RAG gives you search, not cognition.
The layered memory model you describe is what actually works in practice - we use ephemeral conversation buffers, persistent user/domain stores, and compressed episodic summaries that decay over time. The forgetting problem is genuinely harder than remembering.
One thing I'd add: the economic moat is real. Two teams using the same base model can deliver wildly different products based purely on context architecture. The model is commoditized - the orchestration is the product.
Great framing of context engineering as backend engineering reborn. That's exactly how it feels building these systems day to day.
Thanks Chovy. Completely agree: once base models converge, context orchestration becomes the real differentiator. Same model, radically different cognition depending on memory layering and decay design...And you’re right in that forgetting is the unsolved frontier...Relevance over time is harder than recall.
In many ways we are just building better minds around smarter models!
"Retrieval is not memory" — this is the distinction most teams miss. We ran into this exact problem building an AI system that processes financial filings. Our initial approach was classic RAG: embed everything, retrieve top-k, hope for the best. It worked for simple queries but completely fell apart when the agent needed to reason across multiple quarters of data or remember user-specific preferences about how they wanted results formatted.
The layered memory architecture you describe is where we landed too. Short-term conversational state, persistent domain knowledge (SEC filing schemas, company hierarchies), and compressed episodic summaries of past analysis sessions. The forgetting problem is genuinely the hardest part — knowing what to prune without losing signal.
Your point about the economic moat is key. The base model is becoming a commodity. The real IP is in the context orchestration layer — how you decide what the model sees and when. That's where the product differentiation lives.
Absolutely Vic! retrieval alone gives you pieces but definitely not understanding.
Layered memory turns fragmented context into coherent reasoning across sessions.
Selective forgetting is brutal but critical and pruning wrong info kills signal.
And yes, the thing is NOT the model anymore...Its how you orchestrate what it actually remembers.
The "retrieval is not memory" distinction is spot on. We hit this exact wall building multi-session AI agents at work — just throwing everything into a vector store and hoping cosine similarity would surface the right context was a dead end. The agent would retrieve tangentially related facts instead of the actually important ones.
What worked for us was exactly the layered approach you're describing: ephemeral conversation state, structured user preferences that persist across sessions, and compressed summaries of past interactions that decay over time. The forgetting part is honestly harder to get right than the remembering.
Thanks Mahima...Exactly, once you separate working memory, long term structured memory and decaying episodic summaries, the agent starts behaving less like search and more like continuity.
And yes, selective forgetting is where real intelligence begins!
First of all thanks for this great article. I really enjoyed reading this.
My pleasure Sanjay 🙏🏻