By Erol Karabeg,
Co-Founder, President @ Authority Partners
December 17, 2025
What if I told you that AI agents can now complete over one million sequential decisions with zero errors? Not “near-zero.” Not “statistically insignificant.” Actually zero!
This isn’t speculation. It’s the documented result of a November 2025 research paper from Cognizant AI Labs that fundamentally changes the conversation about AI reliability in enterprise settings.
For CIOs and CTOs who have been cautiously watching the agentic AI space, wondering when it would mature enough for mission-critical workflows, the answer is now. And the breakthrough isn’t a bigger, smarter model. It’s an architectural pattern that transforms how we design AI systems.
A few months ago, I found myself in a spirited debate at a gathering of entrepreneurs and AI researchers. The founder of a legal research SaaS company was adamant: it’s impossible to develop a 100% accurate AI agent for high-stakes industries, and the stakes in legal work are simply too high to risk it.
I pushed back. Having architected reliable systems for financial services, health-tech, and energy clients, the “high-stakes” argument felt incomplete. Every serious enterprise considers their use cases high-stakes. Many have literal lives or livelihoods on the line. The question isn’t whether stakes are high. It’s whether we can engineer systems that meet those stakes.
The founder’s second claim frustrated me more. He believed that AI accuracy was fundamentally bounded by the probabilistic nature of large language models. I knew from experience that the answer lay in architecture, not model capability. But I didn’t have research to prove it.
Then, in November 2025, a paper landed that changed everything.
Cognizant AI Labs published “Solving a Million-Step LLM Task with Zero Errors”, introducing what they call the MAKER framework. MAKER stands for Maximal Agentic decomposition, first-to-ahead-by-K Error correction, and Red-flagging.
The headline result: the framework completed a task requiring exactly 1,048,575 sequential AI decisions with zero errors. Not one million decisions with a handful of mistakes. Zero errors, period.
This wasn’t achieved with a frontier reasoning model or a massive compute budget. The researchers used smaller, more efficient models (like GPT-4.1-mini) and beat the performance of expensive monolithic approaches at lower cost.
The implications for enterprise AI deployment are profound.
For decades, enterprise software has pursued reliability through deterministic code. If-then rules execute identically every time. You can trace, predict, and guarantee outcomes.
AI agents broke this model. Large language models introduce probabilistic reasoning that can fail unpredictably. Even at 99% per-step accuracy (which sounds impressive), the mathematics become catastrophic at scale.
Consider the compounding:
– 100 sequential steps at 99% accuracy yields only 37% end-to-end success
– 200 steps drops to 13%
– 500 steps approaches zero
A mortgage origination workflow involves approximately 140 discrete decisions. A claims processing pipeline might involve hundreds more. At traditional AI accuracy rates, these workflows simply cannot run autonomously.
This explains why Gartner predicts 40% of agentic AI projects will be abandoned by 2027, and why Carnegie Mellon research found that leading AI agents fail 70% of standard office tasks. The technology works at the individual step level. It fails at the workflow level.
Until now.
MAKER achieves zero-error performance through three synergistic design principles.
Principle 1: Maximal Decomposition
Rather than assigning complex multi-step reasoning to a single agent call, MAKER breaks every workflow into the smallest possible atomic operations. Each “microagent” performs exactly one task: extract one field, validate one format, execute one calculation, make one API call.
This eliminates context drift and reasoning confusion. Each microagent receives only the information needed for its single operation. Nothing more.
Principle 2: Consensus Voting
Each atomic step executes through multiple independent agent instances. A result is accepted only when one answer leads by k votes. With three agents voting and two agreeing, you proceed. If agents disagree, additional votes are collected until consensus emerges.
The mathematics are transformative. A single agent with 95% accuracy becomes a voting ensemble with 99.7% accuracy at k=3. At k=5, accuracy reaches 99.9997%. At k=7, errors become statistically negligible.
Principle 3: Red-Flagging
Any agent response exhibiting structural anomalies (excessive length indicating reasoning loops, malformed output, implausible values) is discarded before voting. The system resamples until sufficient valid responses exist. This catches correlated errors that voting alone might miss.
Here’s what changes for enterprise leaders: 100% AI accuracy is no longer an aspirational target. It’s an engineering specification with explicit cost parameters.
The MAKER framework shifts the question from “Can we eliminate errors?” to “What is the optimal cost allocation between compute and human review?”
The economics are compelling. Consider a mortgage loan requiring 140 atomic steps:
– At k=3 voting, the workflow consumes approximately 500 LLM calls
– At current API pricing for efficient models, this translates to $0.50-2.00 per loan
– Compare this to $3,000-8,000 for human processing
The ROI calculus is straightforward. Spend more on LLM compute, achieve higher automation rates. Route genuinely ambiguous decisions to humans. The tradeoff is explicit, tunable, and defensible.
This reframes enterprise AI from a technical gamble to a financial optimization problem.
Mortgage origination represents exactly the kind of regulated, high-volume, error-sensitive workflow, that we at Authority Partners encounter every day, where MAKER architecture unlocks new possibilities. Let’s trace how the framework applies.
A single loan origination decomposes into approximately 140 atomic agent decisions spanning: application intake, credit verification, income verification, asset verification, property analysis, AUS submission, pricing, fee assembly, and disclosure generation.
Each phase breaks into microagent calls. Income verification, for instance, becomes 13 discrete operations: load document, detect format, execute OCR, validate output structure, classify document type, extract employer name, extract YTD income, extract pay frequency, normalize to monthly income, apply stability rules, and write to the loan origination system.
Not all steps require the same reliability mechanism. They fall into three categories:
The composite outcome: zero errors across 50,000 loans, with 95-97% of all decisions fully automated.
The Defensible Claim
With MAKER architecture properly implemented, enterprises can make a specific, quantified claim:
“Our AI-powered workflow processes loans with mathematically guaranteed reliability. Deterministic operations achieve zero-error performance through multi-agent voting and validation. Judgment-based operations achieve 99.9%+ accuracy through consensus mechanisms, with edge cases automatically routed to human review. At 50,000 loans per month, we expect fewer than 50 instances requiring human intervention due to agent uncertainty, a 99.9% full-automation rate with bounded, predictable exception handling.”
This isn’t marketing language. It’s a statement backed by probability mathematics and validated by peer-reviewed research.
The Strategic Opportunity
MAKER architecture unlocks AI agent deployment in domains previously considered too risky.
Financial services: Loan origination, underwriting, claims processing, and compliance workflows involve hundreds of sequential decisions with significant error costs. MAKER-based agents handle the volume with quantified, bounded risk.
Healthcare: Prior authorization, benefits verification, and care coordination require both accuracy and auditability. MAKER’s explicit decomposition creates natural audit trails showing exactly how each decision was reached.
Insurance: Policy administration, claims adjudication, and fraud detection demand both speed and precision. MAKER enables throughput at enterprise scale without sacrificing accuracy.
The pattern extends to any domain where complex workflows execute at high volume and errors carry meaningful consequences.
The path from understanding MAKER to deploying it follows a clear sequence.
First, decompose your target workflow into atomic operations. What appears to be 10 major phases typically contains 100+ discrete decisions when properly analyzed.
Second, classify each operation. Deterministic steps get standard voting. Judgment steps get higher thresholds. Business decisions route to humans. The classification determines your reliability mechanism.
Third, set vote thresholds by risk. Higher k means more LLM calls per step but exponentially better accuracy. The cost-reliability tradeoff is explicit and tunable.
Fourth, implement escalation triggers. When voting fails to reach consensus within a reasonable sample count, the system automatically escalates to human review. This catches edge cases that no amount of voting would resolve.
If you’re evaluating where to start, an Agentic AI Assessment can help identify the top 3-5 opportunities with quantified ROI in about three weeks. For rapid validation, Agentic AI Innovation delivers a working prototype in under a month. When you’re ready to scale, Agentic AI Production teams launch enterprise-ready agents with the right UX, integrations, and guardrails.
The barriers to AI agent deployment in mission-critical enterprise workflows have been technical, not conceptual. The concern that AI introduces irreducible error into high-stakes processes was legitimate. Until now.
MAKER architecture reconciles probabilistic AI with enterprise reliability requirements. By decomposing workflows to atomic operations and applying statistical error correction at each step, probabilistic reasoning becomes deterministically reliable. The underlying AI remains stochastic, but the system-level behavior becomes predictable and controllable.
One hundred percent accuracy is no longer an unachievable ideal. It’s an engineering specification with known cost parameters. The enterprise decides how much reliability to purchase through compute, and how much to guarantee through human oversight.
That conversation I had with the legal research founder? If we talked today, I’d have a research paper to share. The proof exists. The architecture is documented. The path forward is clear.
The question is no longer whether AI agents can achieve perfect accuracy. The question is whether your organization will be among the first to deploy them.
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