The Post-Agentic World: The Economics of Abundant Intelligence

Posted on January 23, 2026  •  4 minutes  • 811 words

We are entering what I like to call the “Post-Agentic” era.

It’s a subtle shift, but a profound one. For the last year or two, we’ve been obsessed with “agents”: standalone entities that can perform tasks. We spent our time optimizing for agent efficiency, worrying about token costs, and trying to make a single agent smart enough to solve complex problems in one go.

But the economics of intelligence have reached a tipping point. We can finally afford to be “wasteful” with tokens to achieve higher reliability.

The Economics of Abundant Intelligence

It’s actually a classic technical evolution. When a resource becomes cheap enough, we stop trying to optimize for its usage and start optimizing for the outcome. Think back to early computing: we used to write assembly to save bytes and CPU cycles. Today, we run massive JavaScript runtimes and containers because developer time and system reliability are more valuable than the “waste” of memory.

Tokens are the new CPU cycles.

In a post-agentic world, failed attempts cost nothing (or close enough to it). Only successful attempts matter. We are moving away from optimizing for the efficiency of an individual agent and toward optimizing for the throughput of successful changes.

If you have ten agents working on the same bug, and nine of them fail or produce redundant code, but the tenth produces a perfect, mergeable PR that passes all tests. That’s a win. It feels wasteful to our old-school engineering sensibilities, but in this new reality: Redundant effort is affordable; a stalled workflow is the true cost.

To put this into perspective, consider the math behind running multiple parallel attempts:

The Calculation

• Probability of a single failure: If the success rate of one agent call is 70%, the failure rate is 30% (0.30).
• Probability of ten failures in a row: Since the calls are independent, we multiply the failure rates: 0.30^10 ~ 0.0000059.
• Probability of at least one success: 1 - 0.0000059 = 0.9999941.

The Result

By spending roughly $0.50 on ten parallel calls, your success rate jumps from 70% to approximately 99.9994%. That’s a price I’m more than willing to pay to save my time. Tell me if I got my math wrong.

Strength in Numbers: Reducing Hallucinations

One of the most powerful aspects of this shift is how it handles the “hallucination problem.” When you rely on a single agent, its hallucinations are your failures. But when you move to a multi-agent orchestration, the accuracy floor rises significantly.

By forcing agents to debate, peer-review, or verify each other’s output, we can filter out hallucinations before they ever touch the codebase. You can have one agent propose a solution, another attempt to break it, and a third act as a judge. This “adversarial” approach to code generation turns the inherent probabilistic nature of LLMs from a weakness into a strength.

More agents might mean more chaos, but it also means more forward momentum.

CI is the New Source of Truth

In this world of high-volume, AI-generated code, one thing becomes absolutely clear: CI is king.

Automation is the only source of truth. Period. If tests pass, the code can ship. If tests fail, it doesn’t. There’s no room for “the change looks right” or “I’m pretty sure it’s fine” when you’re processing hundreds of potential changes generated by agents.

Agentic changes must be grounded and verified by rigorous, automated tests. This puts an incredible amount of pressure on our CI/CD pipelines. If we’re going to allow agents to “waste” work to find the best solution, our testing infrastructure needs to be fast, reliable, and incredibly efficient.

This might be the catalyst that finally forces us to revisit tools like Bazel or more aggressive monorepo strategies. When the bottleneck shifts from “writing code” to “verifying code,” our tools have to keep up.

The New Challenge: Orchestration

In this post-agentic world, the bottleneck isn’t the AI’s intelligence anymore; it’s the orchestration.

The real magic (and the real difficulty) is in how these agents talk to each other. How do we prevent them from getting stuck in infinite loops? How do we avoid “groupthink” where they all agree on a flawed solution? How do we manage the context and state across dozens of parallel threads of work?

We also have to ask: do we need to change the way we work to accommodate them? Should we structure our code differently to make it easier for multi-agent systems to reason about? Perhaps more modularity, stricter typing, and even more granular test suites aren’t just for humans anymore; they are the APIs for our new agentic workforce.

The post-agentic world isn’t about one AI doing our job. It’s about a swarm of intelligence making progress in parallel, where the human role shifts from “maker” to “orchestrator” and “final verifier.” It’s wasteful, it’s chaotic, and it’s the most exciting time to be an engineer.