… authorities of the folks, by the folks, for the folks …
— Abraham Lincoln, Gettysburg Deal with (1863)
The price of AI is dropping quickly. GPT-4-class capabilities value roughly $30 per million tokens in early 2023; immediately the identical runs beneath $1, and some suppliers are pushing prices beneath $0.10. Throughout benchmarks, inference costs have fallen between 9x and 900x per 12 months, with a median decline close to 50x. Even frontier fashions are getting dramatically cheaper every technology, with open-source fashions following intently behind. And crucially, even when “Nobel-Prize-winning genius-level” intelligence isn’t right here but, the intelligence that suffices for the overwhelming majority of information work is right here immediately, and getting cheaper by the month. At this price, we’re quickly coming into the period of nearly free intelligence—the sort that’s greater than sufficient for on a regular basis information work.
Disclosure: This put up is a perspective led by Aditya G. Parameswaran—an Affiliate Professor of EECS and co-director of the EPIC Knowledge Lab at UC Berkeley—collectively along with his collaborators. It’s half panorama survey and half perspective, and a number of other of the analysis instructions mentioned beneath (together with agentic hypothesis, structured reminiscence, and synthesizing customized knowledge methods from scratch) draw on the authors’ personal ongoing work.
So, what does this new period of near-free intelligence imply for knowledge methods? We consider three new challenges—and alternatives—stem from near-zero inference prices:
Knowledge Methods For Brokers. Brokers will quickly turn out to be the dominant workload for knowledge methods—with swarms of brokers spun up in response to every end-user request. Given variations in traits between brokers and people—or functions appearing on their behalf—how ought to we redesign knowledge methods for such agentic customers?
Knowledge Methods Of Brokers. As brokers begin taking up the majority of information work, a brand new substrate is required for 1000’s of brokers to handle state over long-running duties, coordinate and attain consensus, and cope with failures. What do knowledge methods that reliably and effectively run and handle agent swarms appear like?
Knowledge Methods By Brokers. Brokers are quickly turning into able to synthesizing whole knowledge methods in a single go—which means we are able to rebuild customized methods for every new workload. Verifying that such methods match meant habits is a problem. What does it take to let brokers synthesize knowledge methods we are able to truly belief?
Knowledge Methods For, Of, and By Brokers
Subsequent, we are going to focus on every in additional element, adopted by discussing the intertwined future of knowledge methods and brokers, particularly because the three challenges intersect.
Knowledge Methods For Brokers
An agent querying a database doesn’t behave like an individual or a BI instrument. It performs what we name agentic hypothesis: a high-volume, heterogeneous stream of labor spanning schema introspection, columnar exploration, partial after which full question formulation. With a number of brokers every exploring parts of the speculation area, every consumer request may quantity to 1000s of particular person SQL queries. Now, customers can difficulty ‘high-level’ knowledge duties, e.g., root-cause evaluation—e.g., ‘why did espresso gross sales in Berkeley drop this 12 months’—or exploratory cohort evaluation—e.g., ‘which consumer segments are more than likely to churn subsequent quarter’—every involving a combinatorial area of potential joins, aggregations, and filter mixtures.
Knowledge Methods Redesigned to Extra Successfully Assist Agentic Hypothesis
The requests from these brokers have varied alternatives for optimization. As an example, on a text-to-SQL benchmark with a number of brokers making an attempt every process, solely 10-20% of the sub-plans are distinct. Thus, 80-90% of sub-queries carry out duplicate work. The identical experiments present process success charges considerably rising with extra agentic makes an attempt—so the redundancy is definitely useful. However from the info system perspective it’s wasted work.
An agent-first knowledge system can exploit such properties to assist brokers make progress sooner. It could reuse outcomes throughout overlapping sub-plans, drawing on concepts from decades-old literature on multi-query optimization and shared scans. Or the info system can attempt to satisfice, returning approximate solutions which are ok for brokers to make progress, leveraging work from the AQP literature—or streaming the outcomes of the ultimate or intermediate operators to assist brokers resolve if seeing the remaining is critical or useful.
One other alternative right here is to rethink the question interface fully: as an alternative of brokers issuing a single SQL question at a time, they might as an alternative difficulty a batch of queries, every with its personal approximation necessities. Since enumerating an exponential search area (as within the root trigger or cohort evaluation examples above) isn’t a very good use of agentic reasoning means, maybe knowledge methods ought to help higher-level primitives reasonably than requiring brokers to listing every SQL question explicitly. One thought right here is to attract on DBT-style Jinja macros to offer looping-based primitives for brokers to work together with knowledge methods.
A Caffeinated Military of Brokers Able to Tirelessly Full Your Knowledge Duties
A last alternative right here is to cease pondering of knowledge methods as passive executors of queries; knowledge methods could possibly be proactive, as they possess extra grounding in knowledge and system traits that brokers could lack a priori—they might steer brokers in numerous instructions, present outcomes for associated queries, and likewise present performance-level suggestions (e.g., as an alternative of executing an costly question, the system may first present the agent a latency estimate). The rationale we are able to do that now versus the previous is that an agent can settle for any type of textual suggestions and isn’t anticipating a strict SQL question end result. Actually, the info system may additionally put together each materialized and digital views for an agent prematurely, supplied to the agent as a part of context, as this can be cheaper or simpler than having an agent writer or use them.
Knowledge Methods Of Brokers
Beforehand, we targeted on how brokers work together with knowledge methods. Now, we think about the whole lot else brokers must maintain working: the place they reside, how they keep in mind, how they coordinate with one another, and the way they cope with failures of one another. This agentic substrate is separate from the inference stack powering uncooked intelligence. Nevertheless, the inference stack itself is being abstracted away by means of APIs (e.g., from OpenAI or Anthropic), or, for open-weight fashions, by means of serving frameworks that cover low-level particulars. To date, the agentic substrate has been managed by means of harnesses like Claude Code and Codex, coupled with varied mechanisms to retailer and retrieve reminiscence.
First, on the reminiscence entrance, the present knowledge is that recordsdata are all you want; brokers write to unstructured markdown (MD) recordsdata, which may then be searched utilizing grep, or by way of embedding-based retrieval. Actually, many argue that the answer to continuous studying is having brokers eat lots (e.g., a complete codebase, slack, firm wikis, …) after which write their learnings into MD recordsdata, that are then retrieved selectively on demand. Certainly, file methods, bash scripting, and MD recordsdata are and can nonetheless be essential for brokers. Nevertheless, at scale, when brokers are doing the overwhelming majority of information work, this method will not be efficient.
Given restricted context home windows, retrieving all MD file fragments that could be related and stuffing it into the context will break down in some unspecified time in the future. Even when context home windows proceed to develop, there are latency advantages to not put all data into context — and in lots of instances, e.g., when information work includes interacting with giant databases or code bases, will probably be infeasible to serialize all related knowledge into context.
Knowledge Methods As A Substrate for Multi-Agent Swarms
One may use a information graph illustration, however information graphs undergo from the identical limitations as unstructured MD-based reminiscence because of their lack of structured search. What one wants is to have the ability to retrieve solely reminiscence that’s pertinent to the duty, throughout a number of attributes (or aspects) of curiosity. For instance, an agent debugging a flaky take a look at ought to be capable of pull solely the reminiscences tagged with the related module, language, framework, and failure mode—reasonably retrieving primarily based on key phrases or embedding similarity. A separate difficulty is what to truly retrieve; uncooked agent traces with errors will not be very helpful as they are going to induce brokers to repeat the identical mistake—as an alternative, we would like the retrieved reminiscence to be corrective.
We not too long ago explored a associated notion of structured reminiscence, the place we manage reminiscence throughout varied attributes, every of which could possibly be set as * to point common applicability, or set as an inventory of values to be matched. For an information agent, the size may embrace the columns and tables, kind of operation, and eventually, open-ended natural-language corrective directions. So, we may embrace reminiscence that solely applies to a given kind of operation (e.g., ‘when performing date-time operations, use fiscal 12 months versus calendar 12 months conventions’), or a given desk (e.g., ‘column product_cleaned is most well-liked over column product when querying on product title’). One open query is defining an application-specific structured reminiscence—or what others have known as world fashions for reminiscence. We consider that is akin to defining a schema for every utility—and maybe brokers themselves may also help us outline and refine it over time.
One Attainable Approach To Retailer and Retrieve Structured Information [From Here]
Structured reminiscence will probably be helpful additionally for evolutionary frameworks to successfully handle search areas. Certainly, storing, structuring, and mining giant volumes of single and multi-agent traces may also help future brokers turn out to be rather more environment friendly—doubtlessly enabling efficient recursive self-improvement by means of structured memory-based mechanisms.
One other problem is to help concurrent edits to shared reminiscence, and concurrent edits usually, when there are a lot of brokers performing transformations. Whereas there have been some helpful makes an attempt at supporting multiversioning and copy-on-write semantics, it isn’t clear that such methods will suffice when 1000’s of brokers try to edit shared state on the similar time. As an example, when brokers try varied potential transactions in response to a consumer request, the consequences of the overwhelming majority of those transactions should be rolled again—with solely the one ‘right’ transaction’s end result persisting. Work on supporting exactly-once semantics is related right here, as are underlying methods primarily based on CRDTs and operational transformation. For updates to fuzzy mechanisms similar to reminiscence, we might be able to sacrifice on consistency for excellent correctness within the curiosity of latency. Whereas brokers can cause about semantics to compensate or roll again their actions to finally finalize most duties, the first problem lies within the diploma to which they step on one another’s toes in the course of the course of. An essential failure mode to be averted is a type of “livelock,” the place incessant compensating actions stop any significant progress.
Past shared state, different considerations emerge when making an attempt to help a military of brokers, together with what to do when brokers fail, how brokers ought to talk with one another (straight or by means of intermediate shared state), and the way we should always cope with straggler brokers. There have been some developments in supporting sturdy multi-agent execution, similar to Temporal, however it stays to be seen if such options will apply at scale throughout 1000’s of brokers. On the subject of communication, we’d like mechanisms to allow brokers to barter with one another. Think about 4 developer brokers making an attempt to succeed in consensus on a shared schema, with distinct however overlapping targets. In a human setting, this might contain iterative dialogue and compromise; for agentic swarms, we should outline the mechanisms that enable them to converge on a design that displays the underlying targets of their respective principals. Or if brokers are all requiring entry to a restricted useful resource, once more communication will probably be essential. It stays to be seen if that is greatest accomplished by way of centralized coordination, or if a decentralized method is critical.
Knowledge Methods By Brokers
Lastly, if intelligence is successfully free, then we are able to make use of this intelligence to synthesize new knowledge methods from scratch. Certainly, in lots of settings, general-purpose knowledge methods could also be overkill, as they need to help each schema, question, and {hardware} goal. Given a workload, current work, together with Bespoke OLAP and GenDB, has proven that one can use an agentic pipeline to synthesize a whole, workload-specific analytical engine—in minutes to some hours, at a price of some {dollars}. The engines are disposable: when the workload shifts, one can merely regenerate them. Analogously, our work has proven that one can synthesize customized key-value shops from scratch, focused to the workload. Actually, trendy IDEs, similar to Kiro, elevate specs for methods growth to be a first-class citizen.
Brokers Can Synthesize Customized Knowledge Methods From Scratch
The primary difficulty, nevertheless, is that specs are sometimes imperfect, and don’t cowl all nook instances. Current-day brokers will exploit the lacking specs to reward-hack their approach to a excessive efficiency metric. In our customized key-value retailer work, we discovered that one approach to alleviate that is to have auxiliary verification brokers making an attempt to generate take a look at instances that catch the exploitation of nook instances, primarily increasing the specification. Yet one more method is to each generate a system and a proof for its correctness collectively, for which we have now discovered some early success, however extra must be accomplished to solidify the method. Additional, it stays to be seen what’s one of the best ways to solicit human-written specs for a system—can this be accomplished in an iterative, human-in-the-loop method, versus a one-shot, incomplete one. Certainly, human-written specs are incomplete even for manually authored software program, so one would anticipate that future brokers which are extra aligned will more and more train higher judgement when making design choices.
One Attainable Knowledge System Synthesis Pipeline [From Here]
Different questions right here contain testing whether or not ranging from a mature system (e.g., Postgres) and eradicating parts/performance can result in larger efficiency or extra consumer belief. Individually, is there a possibility to make the design composable, comprising varied verified parts which are combined and matched given a workload? For instance, maybe the workload hasn’t modified sufficient for the storage layer to be up to date, however maybe the question optimizer requires adjustments. A maybe extra viable proposition includes using brokers coupled with proof methods to focus on crucial components of the code related to formal proofs, reasonably than doing so for the complete system.
A last alternative right here is to maneuver away from the standard knowledge methods stack with clearly-defined interfaces (e.g., parser, question optimizer, storage supervisor, …) — that have been every largely the prerogative of a single human workforce to handle. As an alternative, brokers can discover new methods to “mix” these parts collectively, maybe figuring out new optimization alternatives consequently. Brokers also can fill in lacking gaps in performance to make current methods rather more feature-complete, or attain feature-parity with different competing methods—or analogously, constantly refining open-source methods in response to characteristic requests or points (maybe filed by different brokers!) Doing so in a means that prioritizes correctness, long-term upkeep, and human interpretability will probably be a problem.
Trying Additional Forward
Within the period of near-free intelligence, knowledge methods matter greater than ever. As brokers tackle the majority of information work, the workload for knowledge methods will change, the substrate they should run on should be constructed, and more and more, they are going to take part in designing knowledge methods themselves. Every of those shifts opens up a brand new, thrilling analysis agenda.
Co-Evolution of Knowledge Methods and Brokers
Trying additional out, the boundaries between brokers and knowledge methods will possible begin to blur. As an example, brokers could design the info methods they themselves run on, defining each the interfaces in addition to the system parts beneath. Each the interfaces and internals will be advanced over time by brokers in a type of recursive self-improvement. There may be additionally a possibility to rethink knowledge methods as a holistic supply of reality for everything of related state: together with uncooked knowledge, reminiscence, and coordination state, additional erasing the distinctions between the info that’s being queried by brokers and knowledge generated on account of agentic exercise. Lastly, knowledge methods could themselves incorporate agentic parts, essentially evolving from passive computation engines into clever, proactive, self-optimizing architectures. It’s exhausting to foretell what the long run could maintain. We’re in for a wild journey!
Acknowledgments
The attitude and ongoing work described on this put up are the product of joint analysis and lots of discussions with great collaborators on the EPIC Knowledge Lab, Knowledge Methods & Foundations group, and the broader Berkeley AI-Methods group. Thanks all!
BibTex for this put up:
@misc{intelligence-is-free-blog,
title={Intelligence is Free, Now What? Knowledge Methods for, of, and by Brokers},
writer={Aditya G. Parameswaran and Shubham Agarwal and Kerem Akillioglu and Shreya Shankar
and Sepanta Zeighami and Rishabh Iyer and Matei Zaharia and Alvin Cheung
and Natacha Crooks and Joseph Gonzalez and Joseph Hellerstein and Ion Stoica},
howpublished={url{https://bair.berkeley.edu/weblog/2026/07/07/intelligence-is-free-now-what/}},
12 months={2026}
}
