The New AI Stack: Why Orchestration Matters Most

In AI, success is no longer determined solely by selecting the largest language model or achieving the highest benchmark score. Increasingly, organizations are deploying AI systems composed of multiple foundation models, retrieval systems, APIs, business applications and autonomous agents working together to complete complex tasks.

As these systems become more autonomous, reliability becomes a systems engineering challenge rather than simply a model evaluation problem. According to the National Institute of Standards and Technology’s AI Risk Management Framework, trustworthy AI requires organizations to continuously monitor, govern and manage risks throughout the lifecycle—not simply evaluate a model before deployment. Those principles become even more important as organizations adopt agentic AI capable of making decisions and invoking external tools.

Members of the Senior Executive AI Think Tank, with expertise in machine learning, generative AI, and enterprise AI applications, believe orchestration is rapidly becoming the operational foundation that makes this possible. Rather than simply routing requests between models, orchestration determines how systems select models, validate outputs, monitor execution, recover from failures and balance competing priorities including latency, quality, security and cost.

In the following sections, they explore the critical decisions leaders must make as AI systems become more autonomous—from improving routing and evaluation to strengthening observability, managing complexity and creating safeguards that allow organizations to scale AI with confidence.

Reliability Begins With Intelligent Coordination

For Maitrik Patel, Senior Engineering Manager at Apple, the industry’s biggest reliability challenges are no longer rooted in model quality alone. Patel argues that coordination failures now represent the greatest operational risk.

“Orchestration is already the reliability layer; most teams just haven’t recognized it yet.”

Rather than viewing orchestration as simple routing logic, Patel describes it as the contract governing every interaction across an AI system.

“The key failures are not model failures but coordination failures,” he says. “These include using the wrong model with the wrong latency budget, stale data moving through downstream steps and a fallback that activates correctly but results in a worse experience than a clean failure would.”

Patel believes orchestration must continuously evaluate quality before responses ever reach users.

“It acts as a contract covering latency, quality and fallback behavior across every component.”

He adds that “real-time evaluation” should become part of execution itself rather than a post-deployment measurement.

“The systems that will be reliable at scale are the ones where orchestration knows in real time whether the output it is about to present meets the necessary standards—not just whether the component produced a response.”

“Agents are only as capable as the context they receive. When that context is rich, structured and policy-aware, orchestration becomes more reliable.”

– Tal Lev-Ami, Co-Founder and CTO of Cloudinary

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Build Reliability Before Runtime

While orchestration often receives attention for what happens during execution, Tal Lev-Ami, Co-Founder and CTO of Cloudinary, argues that reliability begins long before agents make their first decision.

“The more intelligence you build into your infrastructure before runtime,” he says, “the stronger and more reliable your orchestration layer.”

Because modern AI agents depend entirely on the quality of their context, Lev-Ami believes organizations should focus on creating structured, policy-aware environments before introducing autonomous decision-making.

“Agents are only as capable as the context they receive. When that context is rich, structured and policy-aware, orchestration becomes more reliable.”

Rather than pursuing maximum autonomy immediately, he recommends remaining closer to deterministic workflows that have been thoroughly tested.

“Most production systems should sit closer to the deterministic end: pre-tested workflows where agents invoke capabilities already defined.”

Lev-Ami outlines three practical principles for organizations scaling agentic AI. First, automate only reversible tasks while requiring validation for consequential actions. Second, design agents that acknowledge uncertainty instead of inventing answers. Finally, ensure every action remains fully auditable.

“Maintain complete auditability because transparency is critical—and the mechanism that makes trust possible.”

Today’s Orchestration Solves a Problem Models Still Can’t

Charles Yeomans, CEO and Founder of Atombeam, believes orchestration has become indispensable—but largely because today’s AI models still lack an important capability: recognizing the limits of their own knowledge.

“Orchestration is a tax we pay on models that guess,” he says. “Every router, evaluator, monitor and fallback exists because the model underneath can return something confident and wrong—and cannot say when a question runs past what it knows.”

In practice, that means organizations should continue investing in orchestration while recognizing its purpose.

“Do the engineering, because today it is necessary: Route by measured capability, evaluate against ground truth and fail toward safe rather than clever.”

Looking ahead, Yeomans expects orchestration to evolve rather than disappear. As reasoning models become better at expressing uncertainty and explaining their decisions, more responsibility may shift into the models themselves.

“The need now is for AI that carries reliability inside the reasoning layer. When that exists, orchestration shrinks back to coordination and most of the tax gets refunded.”

His perspective highlights an important distinction: Today’s orchestration platforms should not simply optimize performance—they should also compensate for the probabilistic nature of modern AI.

Treat Orchestration Like a Product

For Chandrakanth Lekkala, Principal Data Engineer at Narwal.ai, orchestration deserves the same executive ownership and product discipline organizations already apply to customer-facing platforms.

“The orchestration is definitely the layer of reliability for production AI. The model becomes less important—its intelligent coordination more.”

Having built cloud-native AI platforms processing more than 100 million real-time events daily, Lekkala says routing decisions must account for far more than model quality.

“Any orchestration at scale for consumers requires the ability to dynamically route tasks to the appropriate model that considers cost, latency and capabilities.”

He also challenges traditional approaches to model monitoring.

“Evaluation should be continual, not periodic,” he says. “Monitoring should involve following reasoning chains rather than outputs.”

Organizations should automate fallback behavior using confidence thresholds while assigning explicit ownership for orchestration itself.

“The only way to manage complexity is to manage orchestration as a product and make someone responsible for it rather than just give it to the person who built the pipeline.”

As AI systems grow more distributed, that recommendation may prove increasingly important.

Observe the Entire Execution Path

Sathish Anumula, Enterprise and Business Architect at IBM Corporation, agrees orchestration has become the foundation of production reliability—but cautions against treating it as simply another middleware layer.

“Orchestration isn’t a layer you add. It’s where the reliability of the whole system actually lives.”

Because every AI component is probabilistic, he says organizations must evaluate complete execution traces rather than only final outputs.

“Evaluate the trace, not the response. In a multi-step system, the final output can look fine while step three quietly hallucinated a parameter,” he says. “You need to be able to replay the whole path.”

Anumula also emphasizes designing fallback strategies around business risk rather than technical convenience.

“Fallbacks should fail closed on things that matter. A retry loop that silently swaps in a weaker model is worse than an error if the user can’t tell.”

Perhaps most importantly, he warns leaders not to let complexity grow unchecked.

“Every tool an agent can call is a surface area. Teams add capability faster than they add observability to see what it’s doing.”

As enterprises embrace increasingly autonomous AI workflows, Anumula’s advice reinforces a recurring theme across the Think Tank: Orchestration should improve visibility as much as it improves automation.

“Orchestration is becoming the operational backbone of production AI. The model itself is only one piece.”

– Brock Murray, Co-Founder of seoplus+

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Reliability Depends on Governance, Not Just Models

For Brock Murray, Co-Founder of seoplus+, orchestration succeeds when organizations focus less on showcasing sophisticated models and more on creating operational consistency. As AI systems incorporate additional APIs, workflows and external services, he says governance becomes the differentiator between experimentation and production readiness.

“Orchestration is becoming the operational backbone of production AI. The model itself is only one piece.”

Murray argues that enterprises should prioritize dependable performance over always selecting the most advanced model available.

“Long-term reliability depends on how effectively a system routes requests, manages context and makes decisions across multiple components,” he says. “At the consumer scale, delivering consistent, dependable outcomes is far more valuable than simply deploying the most advanced model available.”

Every additional integration expands operational complexity and increases the need for visibility.

“Every additional model, API or workflow introduces another layer of operational complexity. That’s why organizations need strong governance, clear visibility into performance and architectures that can adapt as AI evolves.”

Ultimately, Murray believes orchestration should serve business outcomes rather than technical ambition.

“The objective isn’t to orchestrate more technology; it’s to build AI systems that are predictable, resilient and scalable in the real world.”

Reliability Is Proven on the Second Attempt

Bhubalan Mani, Lead of Supply Chain Technology and Analytics at GARMIN, brings an operational perspective shaped by large-scale enterprise systems where AI doesn’t simply answer questions—it initiates transactions with real-world consequences.

“Retries are easy until the agent has already spent money,” he says. “Orchestration talk stops at routing and evaluation, which are read-side problems. The harder part is the write side.”

In production environments, network failures and timeouts create ambiguity.

“Once an agent calls a tool that moves something real, a timeout is ambiguous: Did it land or not? Without idempotency keys, durable state and replayable execution, the fallback path quietly does it twice.”

Drawing on supply chain examples, Mani illustrates how duplicate purchase orders or repeated inventory transactions can create downstream disruption that no model evaluation metric will detect. His recommendation is to engineer orchestration using proven distributed systems principles.

“Treat every side-effecting tool as a transaction with an idempotency key, keep the orchestrator state durable and resumable and define compensating actions for whatever cannot be made idempotent.”

He concludes with perhaps the simplest definition of production reliability.

“Route by risk, yes. But reliability is decided by what happens on the second attempt.”

Choose the Right Architecture Before Adding More Agents

While many organizations assume multi-agent architectures represent the future of AI, Manpinder Singh Panesar, Senior Solutions Architect at Amazon Web Services (AWS), cautions against adopting complexity for its own sake.

“It may still be too early to call orchestration the reliability layer—or assume every complex system should be multi-agent.”

Panesar points to recent Google research demonstrating that architecture choices depend heavily on model capability and workload characteristics.

“Strong single agents can outperform coordinated systems, while multi-agent patterns add value where work can truly be parallelized.”

Even so, he believes orchestration is rapidly evolving into the control plane governing autonomous AI.

“As agents act autonomously, identity, authorization, observability, evaluation, context management and fallback cannot be afterthoughts.”

Rather than measuring success by the number of collaborating agents, organizations should focus on accountability.

“Reliability will come less from coordinating more agents and more from making every autonomous action attributable, observable, bounded and recoverable.”

“The real challenge is not generating an answer, but knowing which model to invoke, when to use a tool, how to validate outcomes and how to recover gracefully when things fail.”

– Pradeep Kumar Muthukamatchi, Principal Cloud Architect at Microsoft

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The Next AI Race Will Be Won Between the Models

Pradeep Kumar Muthukamatchi, Principal Cloud Architect at Microsoft, believes the industry’s competitive advantage is shifting away from model size toward system design. Drawing on his experience helping startups and enterprises scale secure AI globally, he argues that orchestration is becoming the intelligence layer responsible for turning model capability into business trust.

“The next AI race won’t be won by the model with the most parameters. It will be won by the system that makes millions of autonomous decisions reliably.”

As AI systems evolve into interconnected networks of agents, tools and data sources, orchestration becomes responsible for every critical decision that occurs between user input and final output.

“The real challenge is not generating an answer, but knowing which model to invoke, when to use a tool, how to validate outcomes and how to recover gracefully when things fail.”

Balancing competing priorities—including latency, quality, security, resilience and cost—requires continuous decision-making rather than static workflows.

“At consumer scale, orchestration is the new operating system for AI, continuously balancing quality, latency, cost, security and resilience.”

For executives, Muthukamatchi believes the implication is clear.

“In the agentic era, reliability won’t be defined by model performance, but by the quality of decisions made between models.”

Make Complexity Observable, Not Invisible

Divya Parekh, Founder of executive coaching brand DivyaParekh.com, approaches orchestration from the perspective of executive leadership and organizational performance.

“I believe orchestration is becoming the reliability layer, but only when it does more than pass requests between models and tools.”

Instead, she says orchestration should become the operating discipline that governs every autonomous decision.

“At consumer scale, it must decide which model to use, verify whether outputs meet the task, enforce permissions, track cost and latency and fail safely when a component breaks.”

She recommends organizations implement risk-based routing so that routine requests follow efficient paths while higher-risk decisions automatically receive additional scrutiny.

“Simple tasks take the fastest path, while higher-stakes actions trigger stronger models, human review or additional validation.”

Supporting technologies—including end-to-end tracing, version-controlled prompts, bounded autonomy and continuous evaluation—help organizations retain confidence as systems evolve.

Perhaps most importantly, Parekh warns executives against allowing sophisticated orchestration to obscure operational reality.

“Good orchestration does not make complexity disappear. It makes complexity observable, testable and controllable.”

Key Strategies for Building Reliable AI Systems

• Treat orchestration as a product, not middleware. Assign executive ownership for routing, evaluation, observability and governance rather than leaving orchestration as an implementation detail.
• Optimize for coordination, not just model quality. Reliable AI systems fail more often because of poor coordination than because of weak models.
• Build reliability before runtime. Structured context, deterministic workflows and policy-aware infrastructure improve orchestration before agents make decisions.
• Evaluate execution, not only outcomes. Monitor reasoning paths, tool usage and intermediate steps rather than relying solely on final responses.
• Route based on business risk. Consider cost, latency, confidence and operational impact alongside model capability.
• Design fallbacks that protect trust. Safe failures are often preferable to silently degrading model quality.
• Engineer for transactional integrity. Durable state, replayable execution and idempotent operations become essential when AI performs real-world actions.
• Choose architecture deliberately. Multi-agent systems are not automatically superior; match orchestration patterns to the workload.
• Balance competing objectives continuously. Effective orchestration weighs quality, resilience, security, latency and cost in real time.
• Make complexity visible. Strong observability and governance allow organizations to scale autonomy without sacrificing accountability.

Turning AI Complexity Into Competitive Advantage

As AI applications evolve from single-model experiences into dynamic ecosystems of models, agents and enterprise systems, orchestration is emerging as the discipline that transforms raw intelligence into dependable outcomes. Success increasingly depends not on deploying the most powerful model, but on coordinating every decision surrounding that model—from routing and evaluation to governance, observability and recovery.

For executives, the opportunity is not simply to deploy more intelligent systems. It is to build systems that know their boundaries, communicate their decisions and recover when things go wrong. The organizations that master orchestration will be the ones that turn AI from an impressive capability into a dependable business advantage.