If you read between the lines of Anthropic’s latest release, you’ll notice they just quietly admitted something massive: LLMs alone aren’t enough to get us to AGI.
For a long time, the industry has been obsessed with "reasoning" as a purely linguistic act. We thought if we just made the context window bigger (100k, 200k, 1 million tokens) the model would eventually be able to "think" its way through anything.
But with the introduction of Programmatic Tool Calling, Anthropic is signaling a fundamental shift in how builders should architect agents. We are moving away from brute-force context stuffing and toward a world where code is the universal adapter for intelligence.
Just like humanity learned to read and write, then evolved to use mathematics and programming to 10x our capabilities, AI is making the same leap.
The next frontier isn't just Reasoning. It's Reasoning + Runtime.
The Death of the Pure LLM Loop
Until now, building a complex agent felt like playing a slow, fragile game of ping-pong.
You ask the agent to do something. The agent asks for a tool. You run the tool. You give the result back. The agent reads it, thinks for a second, and asks for another tool. Model → Tool Call → Result → Model → Tool Call...
This loop is probabilistic orchestration. You are effectively crossing your fingers and hoping the LLM stays on track through 20 different turns of conversation. It’s slow, it’s expensive, and frankly, it’s prone to hallucination.
The concept of programmatic tool calling changes the game. It allows the model to write a script (usually Python) to handle that entire loop in one go.
Instead of asking to "get the file," then "read the file," then "filter the rows," the model just writes a Python script to do all three at once. We are moving from probabilistic orchestration to deterministic orchestration.
Python loops don't hallucinate. if/else statements don't get tired or lazy. By offloading the orchestration logic to code, we make agents drastically more robust.
Code is the Universal Adapter
There is another reason this shift is happening: context windows are not infinite hard drives.
Sure, a one-billion-token context window sounds great on paper. But treating an LLM like a database is inefficient. Trying to get an LLM to parse a 10MB log file or a messy CSV using natural language is like trying to do long division with an essay. It’s the wrong tool for the job.
Code is the universal adapter.
If an agent needs to find "the top 5 errors in the logs," it shouldn't be reading the logs. It should be writing a grep command or a Python script to filter them.
- Less tokens to process = Less GPU time.
- Less GPU time = Lower latency.
- Lower latency = Lower cost and energy consumption.
This is a sign of ecosystem maturity. We are finally moving past the brute-force phase of AI development and into the "optimized architecture" phase.
The New Bottleneck: Your Infrastructure
But here is what most builders, and frankly, most cloud providers, are ignoring.
When you move logic from the model (inference) to the runtime (execution), you shift the bottleneck.
Frontier labs like Anthropic are obsessing over the Tokens Per Second (TPS) of their models. They are optimizing the inference loop to be near-instant. But if the model writes a brilliant Python script in 200 milliseconds, and then has to wait 2 seconds for a sandbox to boot up to run it... the magic is lost.
You cannot build instant agents if only half your stack is fast. And the Internet demands instant.
If your infrastructure takes seconds to cold-start a secure environment, your infrastructure is now the bottleneck, not the AI.
Why We Built Blaxel Sandboxes
This is exactly why we obsess over 25ms resume times at Blaxel.
We realized early on that if agents are going to write code, they need a "computer" that is always ready. They can't wait for a ECS container to spin up or a Lambda function to warm up. They need a sandbox that feels like it was already there, waiting for them.
We built Blaxel to be the Perpetual Sandbox Platform. We keep your environments on a "warm standby" that resumes in milliseconds, preserving the entire memory state.
It means your agent can write code, run it, pause, and pick it up again instantly, without you paying for idle compute time.
The Era of Compute
We are entering a phase where the Sandbox is just as critical as the LLM. The model provides the reasoning, while the sandbox provides the reality.
The smartest agents of 2025 won't just be the ones with the biggest brains (context windows). They will be the ones with the fastest reflexes (runtime).
The era of Chats is ending. The era of Compute is just starting.
