The Marrow of Agency

In many ways, agent frameworks can be analogized as the digital “body”: the cyber tentacles which enable “the mind” (i.e. the model) to execute in the world. The marrow of agency.

Encouragingly, the combination is modular in nature – allowing for different combinations of minds and bodies to form to attack discrete problems. Perhaps the strongest tailwind behind agent frameworks has been the accelerating capabilities of the models themselves: growing more adept at using their limbs to accomplish ever more complex autonomous tasks.

Despite early rumors of pre-training’s plateau, the mind is marching on unencumbered, now scaling across numerous vectors:

Better GPUs (H100 -> B200 etc)
GPUs going vertical (expanding pre-training superclusters)
GPUs going horizontal (multi-data center training / distributed training)
RL, Synthetic Data & Inference Test Time Compute (post-training)

The latter has now splashed across headlines from Capitol Hill to ZhongNanHai after the release of R1 by DeepSeek, the first competitive Chinese (and open source) reasoning model.

Not only will scaling continue but it appears poised to accelerate. DeepSeek has proven distillation works: using larger models to train smaller, more cost-effective versions of comparable quality at a fraction of the cost…

Source: DeepSeek R1 Technical Paper

…while simultaneously driving home the point that reinforcement learning without human feedback can provide impressive performance gains.

The cost of intelligence continues to plummet. While twitter is aflame with references to Jevons paradox and what this might mean for Nvidia and other infra / model layer players, the result is unambiguously good for applications.

The agents are coming.

They will have encyclopedic knowledge – now combined with reasoning – for an incredibly low cost. After just over two years, synthetic intelligence is on track to surpass human level intelligence.

Interestingly, these obvious tailwinds are at odds with the vicious web3 agent framework selloff: many dumping as much as 80 – 90% in the last 60 days due to a mix of waning Q4 DeAI euphoria and Q1 macro / tariff uncertainty as Trump throws his weight around.

Web3 Agent Framework Price Performance Since Dec 31st, 2024

Source: Delphi Digital Sector Dashboard (as of Feb 18th)

While each framework has its own short-comings (detailed later), the rapid advance of reasoning capabilities in unambiguously bullish. I would be quite surprised if 2025 did not have a second bout of enthusiasm as these accelerating capabilities are injected into applications, company workflows, and global economies.

We have received fire from the heavens, infinitely replicable digital minds caged in data centers.

We are now going to unleash them.

Matching Making: Unleashing the Mind

Agents are entities which can perceive and act on a specific environment. This requires two way communication streams, short-term and long-term memory, and the relevant context, integrations, and tooling to execute in the environment for which it is tailored.

AI is the brain which helps process tasks, plans actions, and assesses performance. However, as with humans, tools can make agents dramatically more effective: providing “read & write” access to a much greater number of environments.

Note: SWE-agent is a coding agent whose environment is the computer and whose actions include navigation, search, view files, and editing

Source: “Agents” by Chip Huyen

By allowing foundational models to call tools, we enable them with agentic behavior.

In this fantastic write up, Chip Huyen examines three broad categories of tooling that let an agent act on its environment:

Knowledge Augmentation: web browsing, text & image retrieval, read APIs to relevant data sets etc.

Capability Extension: calculators, calendars, code interpreters, etc.

Write Actions: SQL executor, email API for replies, banking API for wire transfers, etc.

While these are singular examples, complex tasks often require sequenced planning capabilities with multiple function calls. As planning is effectively a “search problem” (i.e. forecasting many possible avenues, deciding which is optimal, and “backtracking” as necessary), the recent shift towards “reasoning models” enhanced via CoT and increased inference time compute will make LLMs more capable planners, one of the most important bottlenecks for truly agentic workflows.

way back on friday, the high score on “humanity’s last exam” was o3-mini-high at 13%.

now on sunday, deep research gets 26.6%.

— Sam Altman (@sama) February 3, 2025

Tool selection is also essential. More tools can provide more capabilities, but also require greater mastery and context. Different tasks require different tools, and different models work more seamlessly with certain tools than others. Crafting the precise mix of model, tooling, and sequencing to solve a particular problem is non-trivial.

Agent Frameworks aim to help streamline this process for developers: providing templates, libraries, connectors and other tooling which can be strung together in a modular way to power agents or AI-driven applications.

However, environments in which agents are expected to operate vary considerably. Selecting the right mix of model, tools, and sequencing is critical to building agents which can outperform in a given environment. A mismatch between the framework and use case will often prove more of a hinderance than a boon.

Web2 vs. Web3: Competitive or Symbiotic?

One clearly emerging divide within agent frameworks are web2 leaders like Langchain, Autogen, and CrewAI…

vs. Web3 focused frameworks like ElizaOS, Virtuals / Game, ARC and others…

To date, this divide has manifested in web2 frameworks primarily targeting utility / productivity focused use cases (enterprise workflows, personal assistants, white collar services) while web3 agents have embraced the entertainment / financial uses where crypto has found greater initial product market fit (KOLs, companions, trading).

The productivity-focused profit pool is clearly larger. Today, the TAM is bloated enterprise software contracts, but tomorrow back office, entry-level front office, and even management will be at risk.

This is rapidly underway at many leading firms:

Synthetic intelligence, likely packaged by web2 frameworks, will sweep through the traditional enterprise, capturing most of this profit pool. However, the very impact of this disruption will provide tailwinds for their web3 counterparts.

Both can win.

Death of the Firm: Coase’s Unbundling

AI will rapidly accelerate “gigafication”, pushing ever larger swaths of labor into “the creator economy”: economic activity shifting from “utility driven labor” towards more entertainment / passion / gambling-oriented attention economy.

In 1900, 38% of Americans worked in agriculture feeding 76 million consumers. By 2017, that number had dropped to 1% feeding >300m, over 40% of which are obese, and Novo Nordisk is the most valuable company in Europe.

In the 1960s, manufacturing accounted for ~26% of the U.S. workforce. By January 2025, that number had collapsed to 7.6% of total non-farm employment.

With the AI revolution, we are likely to run this back turbo in services, industries accounting for ~70% of US GDP and ~80% of overall employment.

What comes next?

Productivity will boom within most enterprises as labor is displaced by the marginal cost of compute, leading to a spike in short term profits as margins expand. However, the “great deflation” will soon follow as enterprise moats erode, ceding ground to extremely low cost, highly-optimized, scaled compute providers.

Both sets of frameworks should be well-positioned to benefit from these tailwinds: web2 frameworks directly via subscriptions or API calls as enterprises lean into silicone knowledge work at the expense of their prior enterprise software license + human overseer hybrids. Web3 frameworks indirectly as economic activity shifts into it’s natural domain: entertainment, social, trading, gambling, adult content, etc.

From agri -> manufacturing -> services -> the hyper-gambling / infinitely personalized Netflix & chill economy. Maslow’s hierarchy meets the Last Man.

In time, however, web3 developers and sovereign agents will play a larger role in utility-focused use cases. As industrial era enterprises are unbundled, Coase’s theory of the firm disintegrates. Transaction costs drop to near zero and virtually every task transitions from a subscription / salaried wage to a series of highly specialized on-demand inference cycles; cycles partitioned between a select few mega-firms (Dwarkesh) and an open source mesh of agentic contractors (Me) competing on razor thin margins for the lowest cost compute and any proprietary data advantage (Ben Thompson).

This will take time. To date, web3 agents have largely been relegated to reply slop bots and meme-coin inspired gambling, uncompetitive for enterprise use cases. Much like decentralized storage and compute have been slow to ramp up often due to enterprise reticence, shackled by legal and regulatory risks, web2 platforms like LangChain or Autogen will remain choice frameworks for most developers for the foreseeable future.

However, the regulatory shift under the Trump admin points to early signs of life. As crypto rails and composable compute grow as a fraction of the economy, one would expect frameworks which had optimized for those use cases to gain share. Web3 equips agents with verifiability, sovereignty, and web native payments / capital markets, providing a great sandbox for “free-agents” or multi-agent systems within broader economic value-chains. The enterprise will be swallowed by Big Tech from on-high, and free agent inference cycles from below.

Web3 frameworks will ultimately be valued as some sort of take-rate on the web3 agentic economy.

The Battle of the AI Agent Frameworks

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