Token-maxxing is dead. Agentic memory is what comes next.
Our take
The recent discourse around AI agent development has been, to put it mildly, frenetic. The article from MongoDB, however, provides a crucial dose of perspective, grounding the current excitement in a historical context that’s often overlooked. Comparing the roughly 60 years of database development to the mere 18 months of modern AI agent experimentation immediately highlights just how nascent the field truly is. We're not witnessing a mature ecosystem; we’re standing at the base of a learning curve, still squinting to see the summit. This realization is particularly important as the industry navigates the aftermath of the “token-maxxing” phase, a brief but revealing period where sheer token consumption was mistakenly equated with meaningful progress. As explored in "Meta returns to open source with Muse Glimmer, an Apache 2.0 licensed 30B parameter AI model optimized for agents — available now," the focus is shifting towards efficiency and intelligent resource allocation, a direct response to the unsustainable costs of blindly maximizing tokens. Similarly, "Specification Engineering: The New Skill After Prompt Engineering" underscores the growing understanding that effective AI agent design isn’t just about clever prompts, but about defining clear and robust work specifications.
The core argument presented – that agentic memory, distinct from the context window, is the key to unlocking the next level of agent capability – resonates deeply. The shift from simply stuffing more data into prompts to strategically curating and storing information for future use represents a fundamental architectural change. This mirrors the decades of progress in database technology, where we learned to efficiently store and retrieve structured data based on precise criteria. The challenge now lies in applying that expertise to the unstructured output of generative models, enabling agents to access and leverage past reasoning in a meaningful way. MongoDB’s observation that successful teams are those integrating semantic search and access control natively into their data platforms is a critical point. Stitching together disparate systems is a recipe for fragility and inefficiency; a unified approach is essential for scalability and reliability. The emerging pattern of pairing leaner models with robust memory systems – where the model acts as a judge, evaluating retrieved content rather than generating solutions from scratch – offers a compelling path towards more economical and performant agents.
This architectural shift has profound implications for how we think about agentic economics. By transforming previously expensive, original solutions into readily available, cheap retrievals, memory-powered agents promise a system that becomes cheaper and faster with increased usage – a stark contrast to the linear cost growth associated with token-maxxing. Furthermore, the concept of "typed" memory, mirroring human cognitive organization, is a crucial advancement. Categorizing memories – differentiating between taxonomic knowledge (definitions), procedural knowledge (task lists), and other specialized domains – allows for more targeted and effective retrieval. This curation process, highlighted by the author’s assertion that “the best memories often get there because a human put them there,” brings a welcome dose of realism to the discussion, acknowledging that AI, even with advanced capabilities, benefits from human oversight and refinement. This echoes the lessons learned from knowledge bases and documentation systems – effective information management requires active human participation.
Ultimately, the article serves as a valuable reminder of the long road ahead in AI agent development. While the initial hype surrounding token-maxxing may have faded, the underlying lessons have paved the way for a more mature and sustainable approach. The development of robust, human-curated agentic memory systems, as described by Pete Johnson, appears poised to be the foundational layer that allows us to move beyond the current state of “hand-wiring CGI-BIN.” The question now is: will the industry coalesce around a set of standardized memory architectures and protocols, or will we see a proliferation of proprietary solutions, hindering interoperability and slowing overall progress?
Presented by MongoDB
We have been building databases as an industry for roughly 60 years. We have been building AI agents, in the form most people mean when they say the word today, for about 18 months.
Sit with that ratio for a second, because it explains almost everything about the state of agentic development right now. Six decades versus a year and a half. We are not in the middle of this learning curve. We are standing at the very bottom of it, squinting up.
There is no LAMP stack for agents yet. There is no settled, boring, default set of choices that lets a team stop re-litigating architecture and just ship.
One of the earliest lessons came from the industry’s brief obsession with token-maxxing. For a stretch in early 2026, token consumption became a vanity metric. The backlash was fast. Token volume measures activity, not outcomes.
But the interesting part of the token-maxxing story was never the workplace theater. It was the architectural lesson hiding underneath it.
The context window is the scarce resource
What follows is an aggregation of what I’ve learned from more than 100 customer conversations across 15 cities in six countries during the first half of 2026. I’m seeing organizations begin to converge on the same conclusion: the context window is the scarce resource. The challenge isn’t stuffing more information into every prompt. It’s deciding what belongs there in the first place.
That question has an answer. The answer is memory.
Not the loose way people use that word to mean “the context window,” but a real, persistent, queryable memory system that sits outside the model and feeds it deliberately.
The answer is memory, and it is more than short-term and long-term
A good agentic memory does three things that the context window alone cannot:
It saves what the generative model produced on previous loops and previous sessions, so the expensive reasoning you already paid for does not evaporate the moment the session ends.
It applies role-based access control to that saved content, so a memory created by one team can be shared across an enterprise without leaking things it should not.
It lets new queries retrieve the right prior content, which in practice means it is backed by semantic search rather than exact-match lookup, because agents ask for things by meaning, not by key.
That last point is where this connects back to the 60-years-of-databases observation. We spent six decades getting extremely good at storing and retrieving structured data by exact criteria. Agentic memory needs something different and newer: the ability to store the unstructured output of a generative process and find it again by similarity.
The teams building this well are the ones whose data platform can do semantic search natively, apply access control to it, and hold the generated content in the same place, rather than stitching three systems together with hope.
The pattern that is emerging in enterprises
Once you have memory like that, a genuinely interesting architecture falls out of it, and I am seeing more enterprises converge on it.
You pair the powerful memory system with a leaner model, often an open-weight one, whose job is not to be brilliant but to be a good judge. A new query comes in. The agent does a semantic search on the memory, reranks to get the best candidate answer, and asks the leaner model a single question: is this good enough to return as is, or not?
If it is good enough, you return it. You never paid for the expensive generative model at all. You answered from memory.
If it is not good enough, you escalate to the more expensive generative model, get an original solution, return that, and then save it back into the same memory system so the next session does not have to pay for it either.
Think about what that does to agentic economics over time. Every original answer the expensive model produces becomes a cheap answer the next time someone needs something similar. The system gets cheaper and faster the more it is used, which is the opposite of how naive token-maxxing scales, where cost grows linearly with usage forever. This is the difference between an agent that learns what it already knows and one that re-derives the universe on every loop.
Memory has types, and humans curate the best ones
The last piece, and the one I think separates where we are headed from where we are now, is that mature agentic memory will not be a flat bucket of short-term and long-term. It will have types, the way human memory does.
Taxonomic memory holds terminology, the controlled vocabulary and definitions an organization runs on, so the agent uses "chargeback" to mean what your finance team means by it and not what the internet at large means.
Procedural memory holds task lists and sequences, the how-we-do-this-here knowledge that turns a capable model into a useful colleague. There will be more types than these, and figuring out the right taxonomy of memory types is itself part of the learning curve we are climbing.
And here is the part that should sound familiar to anyone who has run a real production system: the best memories often get there because a human put them there. Not every memory an agent generates is worth keeping, and not every kept memory is worth surfacing first.
Increasingly I expect to see humans curating these systems, injecting the high-value memories back in for frequent reuse, pruning the noise, promoting the procedural sequence that works over the three that mostly work. We did this for knowledge bases. We did it for documentation. We will do it for agentic memory, because curation is how a corpus stops being a landfill and starts being an asset.
What comes next?
We are 18 months, give or take, into agents and 60 years into databases. The gap between those two numbers is not a problem to be embarrassed about. It is just the truth about how early it is, and it should make us humble about every "best practice" that is barely a season old.
Token-maxxing was the first big idea to rise and fall inside this new field, and its fall taught us the lesson the field most needed: the context window is scarce, so the discipline is in choosing what goes in it. That discipline is agentic memory. Semantic-search-backed, access-controlled, typed, human-curated memory that saves what was expensive to produce and serves it cheaply forever after.
There is still no LAMP stack for agents. But if I had to bet on which layer becomes the boring, default, settled choice first, the one we stop arguing about so we can get back to building, I would bet on memory. That is the next advancement in agentic development. Everything else is still hand-wiring CGI-BIN.
Pete Johnson is Field CTO, AI at MongoDB.
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