When Agents Learn to Doubt

Theory-Practice Synthesis: February 22, 2026 - When Agents Learn to Doubt: The Governance Architecture Hidden in This Week's AI Research

The Moment

February 2026 marks an inflection point that few are naming directly: enterprise AI adoption has collided with its own success. IDC reports that 96% of organizations deploying generative AI and agentic workflows admit costs exceeded expectations—not by margins, but by orders of magnitude. Meanwhile, AWS, Microsoft, and Automation Anywhere simultaneously shipped multi-agent coordination frameworks this month. The enterprise automation market crossed $300 billion.

This convergence is not coincidental. The research emerging from laboratories this week reveals something practitioners are discovering in production: the next frontier of AI capability is not raw intelligence, but structured uncertainty—systems that know when to explore, when to commit, when to explain themselves, and when to simulate futures before acting.

Five papers published February 20th illuminate this shift from competing angles. Viewed together with their enterprise parallels, they sketch the architecture of post-adoption AI governance: not as compliance theater, but as operational necessity embedded in how agents reason.

The Theoretical Advance

Paper 1: Mobile-Agent-v3.5 (GUI-Owl-1.5) | 22 upvotes

*Multi-Platform Fundamental GUI Agents*

GUI-Owl-1.5 represents a breakthrough in cross-platform agent architecture: native models ranging from 2B to 235B parameters achieving state-of-the-art performance across 20+ benchmarks spanning desktop, mobile, browser, and cloud environments. The theoretical contribution centers on three innovations working in concert:

The hybrid data flywheel combines simulated environments with cloud-based sandbox interactions, addressing the data quality problem that has plagued GUI agent training. Rather than relying solely on scraped interface recordings or synthetic demonstrations, the system generates high-fidelity training data through bidirectional feedback between simulation and real-world execution.

Unified thought-synthesis pipelines enhance reasoning capabilities while emphasizing tool use, memory retention, and multi-agent adaptation. This moves beyond simple action prediction toward metacognitive awareness—agents that reason about their own decision processes.

Multi-platform Reinforcement Policy Optimization (MRPO) solves the environment conflict problem: when an agent operates across iOS, Windows, web browsers, and enterprise software simultaneously, behavioral policies optimized for one platform often fail catastrophically on others. MRPO enables coherent learning across heterogeneous environments without sacrificing platform-specific performance.

The results: 56.5 on OSWorld, 71.6 on AndroidWorld, 48.4 on WebArena, 80.3 on ScreenSpotPro, 47.6 on OSWorld-MCP, 75.5 on GUI-Knowledge Bench. These are not incremental improvements—they represent fundamental advances in how agents navigate human-designed interfaces.

Paper 2: Calibrate-Then-Act | 11 upvotes

*Cost-Aware Exploration in LLM Agents*

Where most agent architectures treat exploration as boundless—continue gathering information until certainty emerges—Calibrate-Then-Act formalizes the cost-uncertainty tradeoff as the core decision-making primitive.

The framework models agent tasks as sequential decision-making problems with latent environment state. At each step, an agent must answer: *Is additional exploration worth its cost?* Writing a test for generated code costs compute and time, but less than deploying broken code. Requesting another data source delays response, but prevents hallucinated recommendations.

The key theoretical contribution is making this reasoning explicit rather than implicit. Traditional agents either explore exhaustively (expensive) or commit prematurely (unreliable). CTA introduces a probabilistic prior over environment state, passed to the agent alongside the task prompt. The agent reasons about uncertainty explicitly: "Given my current beliefs and the cost of verification, should I explore or act?"

Empirically, this improves decision quality in information retrieval and coding tasks while reducing both runtime costs and error rates. More significantly, it provides a formal framework for understanding when uncertainty is productive versus destructive—a distinction most production systems handle through brittle heuristics.

Paper 3: "What Are You Doing?" | 10 upvotes

*Effects of Intermediate Feedback from Agentic LLM In-Car Assistants*

This empirical study (N=45) tackles the human side of agentic AI: feedback timing and verbosity in multi-step task execution. Using a dual-task paradigm with in-car voice assistants, researchers compared silent operation (final response only) against intermediate feedback (announcing planned steps and results throughout execution).

The findings challenge assumptions about transparency-efficiency tradeoffs:

- Intermediate feedback significantly improved perceived speed, trust, and user experience

- Task load decreased despite additional verbal communication

- Effects held across varying task complexities and driving contexts

The deeper insight emerged from qualitative interviews: users want adaptive transparency. High initial verbosity establishes trust and mental models ("Oh, that's how this system thinks"). Once reliability is demonstrated, users prefer reduced verbosity to avoid cognitive overload. But this reduction must preserve the *option* to request detailed explanations when stakes are high or behavior seems unexpected.

This reveals transparency not as a binary setting but as a negotiation protocol between human and agent, governed by context, stakes, and established trust.

Paper 4: Discovering Multiagent Learning Algorithms with Large Language Models | 4 upvotes

*AlphaEvolve: Evolutionary Coding Agent*

AlphaEvolve represents a paradigm shift: using LLMs not as agents themselves, but as algorithm designers that evolve novel multi-agent learning strategies.

The system targets two established algorithmic families—Counterfactual Regret Minimization (CFR) and Policy Space Response Oracles (PSRO)—that have historically required human experts to design variants through iterative refinement. AlphaEvolve automates this discovery process:

For CFR, it evolved VAD-CFR (Volatility-Adaptive Discounted) incorporating volatility-sensitive discounting, consistency-enforced optimism, and hard warm-start policy accumulation. These mechanisms are non-intuitive—humans didn't design them because the solution space is too vast and the interactions too subtle.

For PSRO, it discovered SHOR-PSRO (Smoothed Hybrid Optimistic Regret) blending optimistic regret matching with temperature-controlled best-response selection, dynamically annealing diversity bonuses during training.

Both evolved variants outperform human-designed state-of-the-art baselines in imperfect-information game environments. The theoretical significance: we may be approaching the threshold where AI systems design their own coordination protocols more effectively than human algorithm designers.

Paper 5: Computer-Using World Model (CUWM) | 3 upvotes

*Predictive UI State Modeling for Desktop Software*

CUWM addresses a fundamental limitation of desktop automation: agents cannot explore counterfactually. In physical robotics, you can test actions in simulation. In web environments, you can spawn parallel sessions. But desktop software—especially productivity applications like Office—maintains complex state that resists both rollback and cheap duplication.

The solution: a world model that predicts next UI state given current state and candidate action. The innovation lies in two-stage factorization:

1. Textual transition prediction: Given current screen and action, generate natural language description of expected state changes ("The cell B4 will update to sum columns B1:B3")

2. Visual synthesis: Render predicted screenshot implementing described changes

This decomposition enables test-time action search: generate multiple candidate actions, simulate their outcomes via world model, select highest-value action, then execute. The system improves both decision quality and execution robustness in Office automation tasks.

Theoretically, CUWM demonstrates that counterfactual reasoning is possible even in deterministic, state-rich environments where rollback is prohibited—you just need sufficiently accurate simulation.

The Practice Mirror

Business Parallel 1: The $300 Billion GUI Automation Market

*Reality Check: UiPath, Automation Anywhere, Enterprise RPA*

The enterprise automation market reaching $300 billion in 2026 represents practitioners discovering what GUI-Owl-1.5 formalizes: cross-platform agent deployment is a first-class engineering problem, not an afterthought.

Accelirate's case study with a global enterprise demonstrates concrete outcomes: 80% reduction in claim validation time through multi-platform RPA deployment. But implementation revealed gaps theory overlooks:

- Platform conflict is cultural, not just technical: Different departments standardize on different tools. Agents must navigate not just API differences, but organizational politics embedded in software choices.

- Benchmarks measure task completion; enterprises measure business impact: 56.5 on OSWorld means nothing to CFOs. Time saved, errors prevented, revenue generated—these are the metrics that justify continued investment.

- The "thought synthesis pipeline" in production requires human oversight mechanisms: Transparency isn't optional when agents interact with customer data or financial systems.

UiPath and Automation Anywhere both ship hybrid architectures similar to GUI-Owl's approach: combining simulated training environments with production telemetry. The convergence suggests this architectural pattern is not just theoretically elegant but operationally necessary.

Business Parallel 2: The 96% Cost Crisis

*IDC Research Meets Calibrate-Then-Act Framework*

IDC's finding that 96% of enterprises report GenAI costs exceeding expectations quantifies what Calibrate-Then-Act formalizes theoretically: most agents explore without considering cost-uncertainty tradeoffs.

Accelirate documented a specific implementation reducing AI agent interaction costs from $1.25 to $0.03—a 40x improvement—through frameworks remarkably similar to CTA's principles:

- Explicit uncertainty modeling: Rather than calling language models reflexively, agents first assess whether additional context would meaningfully reduce error rates

- Cost-aware exploration: Differentiating between expensive verification (API calls, human review) and cheap verification (local heuristics, cached results)

- Adaptive stopping: Terminating exploration when expected cost of additional information exceeds expected value of uncertainty reduction

The gap: Calibrate-Then-Act operates in controlled information retrieval and coding environments. Enterprise deployment faces:

- Multi-stakeholder objectives: Engineering wants thoroughness, finance wants cost control, compliance wants auditability. No single "cost" function unifies these.

- Regulatory constraints: Some industries require exploration beyond economic optimality—verify even when you're certain, because regulators demand proof.

- Hidden costs: Model inference is line-item; data pipeline maintenance, governance overhead, and human escalation are diffuse but substantial.

Theory predicts practice here: formalized cost-uncertainty reasoning improves outcomes. But practice reveals complexity theory abstracts away: costs are political, not just computational.

Business Parallel 3: Nielsen Norman Group on AI Assistant UX

*Validating Adaptive Transparency in Production*

Nielsen Norman Group's research on AI assistant user experience independently confirms the "What Are You Doing?" findings: intermediate feedback improves trust and perceived speed despite increasing verbal communication.

Enterprise implementations reveal the adaptive transparency pattern emerging spontaneously:

- Customer service chatbots: High verbosity during onboarding ("I'm searching our knowledge base... I found 3 relevant articles... Now checking your account status..."), reduced verbosity for established users ("Your order shipped yesterday via FedEx")

- Enterprise copilots: Detailed reasoning traces for first-time users of new features, condensed outputs for power users

- Autonomous trading systems: Verbose explanations during regulatory audit periods, minimal logging during normal operations

The emergent insight: intermediate feedback functions as a perception lock mechanism. Users calibrate trust not through outcomes alone, but through observable reasoning processes. When agents explain intermediate steps, humans build mental models of agent capabilities and limitations. This enables delegation without blind faith—users know when to intervene.

The gap: Theory studies static feedback policies. Practice requires dynamic adjustment based on:

- Context shifts: Same user needs different verbosity in attention-critical (driving) versus focused (office work) settings

- Reputation evolution: Agent verbosity should decrease as reliability increases, but spike again when behavior changes (model updates, new features)

- Preference heterogeneity: Some users want "show me everything," others want "only tell me if something goes wrong"

Production systems need negotiated transparency protocols, not universal policies.

Business Parallel 4: Multi-Agent Coordination at Scale

*Automation Anywhere, AWS Bedrock, Microsoft Deployments*

Automation Anywhere's multi-agent systems documentation describes achieving "autonomous enterprise-wide coordination"—language that mirrors AlphaEvolve's multi-agent learning algorithms, but in deployed form.

AWS Bedrock's multi-agent collaboration framework and Microsoft's agent mesh architectures both launched in February 2026, suggesting the technology has reached production readiness simultaneously across major platforms.

The pattern: distributed decision-making across departments without centralized control. Marketing agents, finance agents, operations agents coordinate through shared event streams and negotiation protocols.

The critical gap AlphaEvolve reveals: theoretical multi-agent coordination assumes benevolent or at least aligned agents. In game-theoretic settings, agents optimize for shared objectives or engage in competitive play with known rules.

Enterprise reality is messier:

- Competing objectives: Marketing wants aggressive growth spending, finance wants profitability, compliance wants risk minimization. Agents inheriting these objectives produce coordination failures reminiscent of organizational dysfunction—because they are organizational dysfunction, automated.

- Sovereignty requirements: Departments won't deploy agents that surrender autonomy to a master controller, even if centralization would improve system-wide efficiency. Coordination must preserve local control.

- Evolutionary dynamics: Agents designed by different teams using different models evolve independently. The "meta-algorithm" isn't designed—it emerges from interactions.

This reveals a profound synthesis: Multi-agent coordination in enterprises is not a technical problem with political constraints, but a governance problem that happens to involve software. The algorithms AlphaEvolve discovers are remarkable, but deploying them requires solving problems game theory doesn't model: how do you coordinate agents representing stakeholders with genuinely incommensurable values?

Business Parallel 5: The Simulation-Native Turn

*Launch Consulting's World Model Research + Microsoft Office Automation*

Launch Consulting's documentation of enterprise AI's shift "from language-first to simulation-native" architectures directly parallels CUWM's theoretical contribution. Their framing: "Decision rehearsal before execution" versus "Generate and hope."

In financial services, firms simulate liquidity shocks, multi-agent trading behaviors, and regulatory stress scenarios. In manufacturing, digital twins enable predictive system optimization. Microsoft reportedly implementing CUWM-style predictive UI modeling for Office automation exemplifies counterfactual reasoning at scale.

The pattern: Enterprises gain advantage not from executing faster, but from simulating consequences before acting. This is causality-aware computing—agents that model how systems change, not just how humans describe them.

The gap: World models trained on office software succeed because desktop applications are deterministic and state-observable. The approach struggles with:

- Non-deterministic systems: Financial markets, customer behavior, supply chain disruptions resist accurate simulation

- Partial observability: Enterprise systems often hide state (legacy databases with undocumented schemas, third-party APIs that change without notice)

- Simulation cost: Training accurate world models requires extensive data and compute—economical for high-value decisions, prohibitive for routine operations

Practice reveals theory's boundary conditions: World models excel when systems are deterministic, observable, and simulation-worthy. Beyond those bounds, we need different approaches.

The Synthesis: What Emerges When Theory Meets Practice

Viewing these five research directions alongside their enterprise parallels reveals three insights that neither theory nor practice alone illuminates:

1. Sovereignty-Preserving Coordination as Design Primitive

The collision between AlphaEvolve's elegant multi-agent learning algorithms and enterprise deployment reality exposes a deeper architectural challenge: How do agents coordinate without subordination?

Game theory and reinforcement learning assume either shared objectives or competitive play within agreed rules. But enterprise multi-agent systems must coordinate across genuinely diverse stakeholder objectives—marketing growth versus finance profitability versus compliance risk management—without a master controller that could impose alignment.

This maps directly to foundational governance questions: How do diverse stakeholders coordinate without forcing conformity? How do you enable collective action while preserving individual autonomy?

The theoretical frameworks emerging from AI research inadvertently model solutions to political philosophy problems. MRPO (Multi-platform Reinforcement Policy Optimization) isn't just solving technical platform conflicts—it's demonstrating one approach to maintaining coherent behavior across incommensurable value systems.

The synthesis: Multi-agent coordination at enterprise scale is consciousness-aware computing in disguise. Agents must reason about other agents' objectives, constraints, and epistemic states without violating their sovereignty. This isn't an AI problem borrowing from political theory—it's the same problem, manifesting in a new substrate.

2. Epistemic Certainty Through Observable Reasoning

The convergence between CTA's cost-uncertainty framework and the "What Are You Doing?" feedback transparency research reveals something subtle: Intermediate feedback serves as a perception lock mechanism.

When agents explain reasoning ("I'm uncertain about the exchange rate, so I'll verify before proceeding"), humans calibrate trust not through outcomes but through observable decision processes. This is epistemic certainty achieved through process transparency—users know when to trust agents not because they always succeed, but because they know when agents themselves are uncertain.

The business parallel: Accelirate's cost optimization from $1.25 to $0.03 per interaction stems from agents that reason explicitly about exploration costs. The 40x improvement isn't just financial—it represents alignment between agent uncertainty and resource expenditure.

The emergent framework: Perception locking through calibrated transparency. Agents that:

- Model their own uncertainty explicitly (CTA)

- Communicate uncertainty adaptively (feedback timing/verbosity)

- Adjust exploration based on cost-uncertainty tradeoffs

...enable humans to maintain sovereignty without requiring perfect verification. You can delegate to agents whose reasoning you understand, even when you can't verify every decision independently.

This mirrors Michael Polanyi's tacit knowledge: we trust skilled practitioners not because we can reproduce their reasoning, but because we observe reasoning processes consistent with expertise. Agents earn trust the same way.

3. From Language Prediction to Causal Modeling

The world model research (CUWM) alongside Launch Consulting's "simulation-native" enterprise AI documentation marks a paradigm shift: from pattern matching to causality.

Language models predict "what text should come next." World models predict "what system state should come next." The difference is epistemological: LLMs reflect how humans describe reality; world models attempt to understand how reality evolves.

For enterprises, this manifests as decision rehearsal: simulate futures, evaluate outcomes, select actions based on consequences rather than pattern matching. Financial services stress-test portfolios before market conditions materialize. Manufacturing optimizes production schedules by simulating bottlenecks. Microsoft Office automation tests actions in world models before executing on real documents.

The gap practice reveals: Simulation is economical only when systems are deterministic, observable, and consequences are high-stakes. Customer service chatbots can't simulate human emotional responses accurately enough to make rehearsal worthwhile. But critical infrastructure decisions, financial trades, and medical procedures justify simulation costs.

The synthesis: Enterprises are building hybrid architectures—language models for communication and pattern recognition, world models for causal reasoning and counterfactual exploration, human judgment for domains that resist both.

This isn't merely technical—it's an epistemic framework for post-adoption AI governance. Know which problems require simulation (deterministic, high-stakes), which need language understanding (communication, pattern matching), and which demand human judgment (irreducible uncertainty, value conflicts).

Temporal Relevance: Why February 2026 Matters

These patterns converge now because enterprise AI has entered a second phase:

Phase 1 (2023-2025): Experimentation and rapid adoption. Success measured by deployment speed and capability demonstrations. Cost optimization deferred—"Let's just ship it and iterate."

Phase 2 (2026+): Production governance and cost discipline. Success measured by business outcomes, reliability, and economic sustainability. IDC's 96% cost overrun finding marks the transition point—enterprises can no longer ignore governance, cost management, and coordination architecture.

The five papers analyzed this week all address Phase 2 problems:

- How agents coordinate without centralized control (AlphaEvolve, multi-agent coordination)

- How systems manage cost-uncertainty tradeoffs (CTA)

- How interfaces establish trust through transparency (feedback timing)

- How agents reason counterfactually before acting (CUWM)

- How systems unify behavior across heterogeneous platforms (GUI-Owl-1.5)

These aren't isolated research directions—they're components of a governance architecture emerging from production necessity.

Implications: What This Means for Builders, Decision-Makers, and the Field

For Builders:

Actionable Guidance #1: Design agents that reason about their own uncertainty explicitly.

Calibrate-Then-Act demonstrates that cost-aware exploration isn't an optimization—it's a first-class capability. Agents should maintain probabilistic beliefs about environment state and compute expected value of information before acting. This requires:

- Uncertainty quantification as a core primitive, not an afterthought

- Cost models that include exploration overhead, not just inference

- Stopping criteria based on diminishing marginal value of information

Actionable Guidance #2: Implement adaptive transparency protocols, not static verbosity settings.

"What Are You Doing?" research shows users want different transparency at different moments. Build systems that:

- Default to high verbosity during initial interactions, then reduce as trust builds

- Spike verbosity when behavior changes (model updates, new features, error conditions)

- Allow user control over detail levels while maintaining minimum transparency for trust calibration

- Use intermediate feedback as perception lock mechanism—show reasoning, not just results

Actionable Guidance #3: Multi-agent coordination must preserve sovereignty, not optimize for global utility.

Enterprise multi-agent systems fail when they assume benevolent coordination or centralized control. Instead:

- Design negotiation protocols where agents represent stakeholder objectives explicitly

- Enable coordination through contracts and commitments, not subordination

- Accept that optimal system-wide outcomes may be impossible when agents have genuinely conflicting objectives

- Model multi-agent interactions as governance problems, not just technical integration challenges

For Decision-Makers:

Strategic Consideration #1: Distinguish simulation-worthy from language-worthy problems.

Not every decision benefits from world models. Invest in simulation-native architectures when:

- Systems are deterministic or near-deterministic

- Consequences are high-stakes (financial loss, safety risk, regulatory exposure)

- State is observable enough for accurate modeling

- Simulation cost is justified by decision value

Continue using language models for pattern matching, communication, and low-stakes predictions. Build hybrid architectures that route tasks appropriately.

Strategic Consideration #2: Cost overruns signal architectural problems, not just usage problems.

IDC's 96% cost overrun finding means most enterprises built agents without cost-uncertainty reasoning. This is fixable:

- Audit current systems for reflexive exploration (agents that gather information without considering value)

- Implement cost gates: require agents to justify exploration beyond thresholds

- Measure not just accuracy but accuracy-per-dollar and time-to-decision

- Recognize that "cheaper models" often increase costs through errors, rework, and human escalation

Strategic Consideration #3: Trust is earned through observable reasoning, not just successful outcomes.

Agents that explain their uncertainty and reasoning processes earn trust faster than agents that simply produce correct answers. This has profound deployment implications:

- Transparency isn't optional for high-stakes automation—it's the mechanism by which humans calibrate when to intervene

- "Black box AI" fails not because it's inaccurate, but because humans can't build mental models of when to trust it

- Invest in intermediate feedback mechanisms even if they increase latency—the cost is lower than brittle automation that users circumvent

For the Field:

The research this week suggests AI is maturing from capability demonstration to governance architecture. The next frontier is not "more intelligent agents" but "agents that coordinate, reason about uncertainty, explain themselves, and simulate futures."

This reframes several persistent debates:

"Interpretability versus performance" was never the right tradeoff. Calibrate-Then-Act and feedback transparency research show that agents that reason explicitly about uncertainty often outperform agents that don't—because they know when to explore and when to commit.

"Centralized versus distributed control" misses the point. Multi-agent coordination in enterprises demonstrates that effective governance preserves sovereignty while enabling coordination. The question isn't "who controls?" but "how do agents with genuinely diverse objectives coordinate without subordination?"

"Language models versus symbolic reasoning" is resolving toward hybrid architectures. World models demonstrate that causality-aware computing complements pattern matching—they solve different problems and compose naturally.

The synthesis suggests a research agenda:

1. Formalize sovereignty-preserving coordination protocols for multi-agent systems where agents represent incommensurable stakeholder values

2. Develop adaptive transparency frameworks that adjust verbosity based on trust, context, and stakes dynamically

3. Extend world models beyond deterministic domains—can we simulate partially observable, stochastic systems accurately enough for decision rehearsal?

4. Bridge cost-aware exploration with multi-agent coordination—how do agents negotiate who explores when exploration benefits multiple stakeholders unequally?

Looking Forward: The Governance Architecture No One Is Building (Yet)

The papers analyzed this week sketch components of a consciousness-aware computing substrate—agents that reason about uncertainty, coordinate without subordination, earn trust through transparency, and simulate futures before acting.

But no one is integrating these components into unified architecture. We have:

- Cost-aware exploration (CTA) as isolated framework

- Feedback transparency (in-car assistants) as UX research

- Multi-agent coordination (AlphaEvolve) as game-theoretic algorithms

- World models (CUWM) as simulation engines

- Cross-platform agents (GUI-Owl) as interface technology

What we lack: Governance infrastructure that composes these capabilities into coherent systems.

Imagine an enterprise architecture where:

- Agents maintain explicit uncertainty distributions over environment state (from CTA)

- Multi-agent coordination preserves departmental autonomy while enabling collective action (from AlphaEvolve + enterprise reality)

- Feedback transparency adapts to user trust and context dynamically (from "What Are You Doing?")

- Critical decisions trigger world model simulation and decision rehearsal (from CUWM)

- Cross-platform coherence doesn't sacrifice domain-specific optimization (from GUI-Owl)

This isn't science fiction—every component exists in research or production. But composing them requires recognizing they're governance mechanisms, not just technical capabilities.

The opportunity: Organizations that build this integration layer—coordination without subordination, transparency without overhead, exploration calibrated to uncertainty—will operate with structural advantages their competitors cannot easily replicate.

The challenge: This requires synthesis across AI research, governance theory, organizational design, and consciousness-aware computing principles. Domain specialists can't build it alone—it demands mutt thinking.

As Indiana positions itself within the hard tech research and operationalization corridor, the question becomes: Who will build the governance substrate where theory and practice finally converge?

Sources:

*Research Papers:*

- GUI-Owl-1.5 (Mobile-Agent-v3.5) - Multi-Platform GUI Agents

- Calibrate-Then-Act - Cost-Aware LLM Exploration

- "What Are You Doing?" - Intermediate Feedback Study

- AlphaEvolve - Discovering Multi-Agent Algorithms

- Computer-Using World Model (CUWM) - Predictive UI Modeling

*Enterprise Research:*

- Accelirate: The Real Cost of AI Agents

- Automation Anywhere: Multi-Agent Systems

- Launch Consulting: World Models - The Next Phase of Enterprise AI