Agentic Orchestration: Stop Chasing Agents, Start Delivering Outcomes

Agentic Orchestration: Stop Chasing Agents, Start Delivering Outcomes

Just as “42” is NOT the answer to the ultimate question of life, the universe, and everything, as Douglas Adams told us in his brilliant novel The Hitchhiker’s Guide to the Galaxy, MCP and A2A are not the answers to understanding agentic orchestration, let alone the solutions to get AI finally to autonomy. The ambition of Agentic AI is to build AI systems capable of autonomous decision-making and actions. Agentic orchestration isn’t just an IT problem, it is a workforce transformation. Therefore, regardless of the marketing claims of technology providers, organizations must plan how to operationalize Digital Labor. In an industry that is predominantly based on labor arbitrage, this heralds a fundamental change. Suffice it to say, we have been here before. Yet, what was largely lip service when we were discussing RPA and Intelligent Automation is now becoming the focal point for scaling AI deployments. Translated into the parlance of Agentic AI, designing a multi-agent workforce means the right tasks, assigned to the right agents (human or AI), at the right time. In a series of blog posts and research notes, PAC will deconstruct agentic orchestration based on a series of interviews with SIs and ISVs. This post will be followed by reflections on segmentation issues and on the operating model for Agentic AI. The last blog will provide a framework as well as an outlook for future developments.

Taking stock of agentic orchestration

What constitutes agentic orchestration is not just in the eye of the beholder but a question of context. Today’s leading definitions of agentic orchestration converge on a simple insight: it’s not just about deploying individual AI agents—it’s about creating an intelligent, adaptive ecosystem. Whether framed as a conductor, a governance layer, or a unified orchestration engine, the value lies in coordinated autonomy—intelligent systems that work together under robust guardrails, enabling seamless collaboration across bots, models, and humans. The magic happens when orchestration becomes enterprise-grade—with monitoring, auditability, fallback strategies, and dynamic routing—not just yet another integration challenge. What sounds simple on paper or in PowerPoint is challenging the maturity of operational teams in most organizations, as Santhosh Coimbatore Viswanathan, Director of AI GTM – Europe at TCS, outlined: “Customers are overwhelmed by the proliferation of agents offered by hyperscalers, ERPs, workflow tools, and niche vendors in an enterprise context. The biggest challenge isn’t building agents – it is deciding which ones to integrate and how to orchestrate them effectively to achieve maximum value.”

Today, most enterprises use agents as assistants, not decision-makers. Similarly, most enterprises succeed with single, bounded agents solving narrow, high-value tasks. Thus, all too often, there is no evolution from assist to augment to transform, but plain and simple, a confusion with knowledge-centric use cases delivered by LLMs. In a nutshell, there is confusion between task automation, agentic workflows, and true agentic orchestration. Yet, scaling orchestration means giving agents more autonomy, which requires radically different governance mechanisms. Agentic orchestration is not just another automation layer – it fundamentally reshapes how organizations design, manage, and govern work. While vendors pitch marketplaces and orchestration platforms, business value only emerges when enterprises redesign operating models around AI-driven ecosystems. Yet, this is largely still the North Star for most organizations. Enterprises want end-to-end automation, but current solutions and the maturity level of organizations relegate adoption to task and step-level automation at best. But the trajectory is unmistakable: as frameworks mature, multi-agent ecosystems will underpin outcome-driven orchestration. The winners will be those who combine context engineering, governance, and adaptive operating models to move beyond productivity to capturing business value.

Yet, there is another fundamental issue to address. We need experiences and narratives from where agents meet technical, process, and cultural debt. Just pitching demos of cloud or even AI-native environments helps to sketch out the direction of travel, but it does not reflect the reality for most organizations. Simply put, it is not about the number of agents but about the outcomes achieved. As with cloud, agents as part of ISV platforms are a welcome lever to drive efficiency gains, but they alone won’t overcome the GenAI Paradox, as McKinsey calls it, the suggestion that widespread AI adoption isn’t producing material business gains.

The Agentic AI reality check

To understand the opportunities and challenges of agentic orchestration, we must honestly examine the limitations of the underlying technologies and thus cut through the market noise. While the progress of Generative and Agentic AI is, at times, stupendous, the agency and autonomy offered by those technologies are not well understood. The challenges of Agentic AI focus on three key issues:

• First, the primary limitation of contemporary AI systems is their lack of causal understanding. By relying on correlative pattern recognition, rather than causal reasoning, they curtail contextual adaptability but also lead to low levels of explainability.
• Second, the limited contextual understanding, the inability to understand and respond appropriately to varying situations. The adaptability problem becomes particularly evident in rapidly changing business environments.
• Lastly, AI agents often fail at longer tasks and rarely recognize when they’re off-track or adapt effectively after failure.

Pivoting from productivity to value

So where do organizations go from here? Paul Roehrig, Chief Strategy and Marketing Officer at Ascendion, outlined why it is essential to move beyond a productivity mindset for organizations to capture value: “AI agents in action change the relationship between humans and bots; changes HOW work is done. This transformation is worth it because, sure, we’ll save cost, but – more importantly – we can increase innovation velocity. That means growth for business leaders and impact on all of us. We know it’s not a myth because it’s starting to happen, but we’re still in the early innings.” Not only that, but we must reimagine automation. My mentor, Chris Boos, now Managing Director at Almato AI, passionately makes this point. As he points out, fundamentally, all AI is automation. However, to leverage the potential of goal-driven automation and of non-deterministic decision-making that is inherent in Agentic AI, you cannot simply continue trying to automate and scale standardizations. Therefore, it is imperative to reimagine work and the operating models if organizations want to capture value from Agentic AI. In his view, that is the unique opportunity to expand economic activity. Going down the path of just augmenting processes by adding agents on top of existing processes, as bolt-ons to chase productivity, will at best provide tactical gains and incremental cost savings. In other words, augmentation might improve efficiency, but doesn’t fundamentally unlock new capabilities and consequently value. Put bluntly, augmentation is incrementalism that is reminiscent of the RPA playbook.

Deconstructing Agentic Orchestration

So, what is the impact of Agentic AI, and what is the strategic value of agentic orchestration? The market noise oscillates between tales of the one-man unicorn and the swansong for management consultants, to the other extreme of a recent MIT survey suggesting that 95% of organizations are getting zero return on their often steep investments. If truth be told, it is difficult to get a good handle on where adoption really is, because there are very few client examples of scaled multi-agent orchestration. In contrast, there are many ISVs’ demos and provider claims on the number of agents and the simplicity of deploying an agent. Therefore, to get more clarity as to where organizations really are on their transformation journey, a trip down memory lane with the many challenged cloud transformations provides valuable pointers. Two-thirds of those transformations didn’t achieve their objectives because technology and business objectives were not aligned. Consequently, only 1 in 4 enterprises can demonstrate a hard ROI on business benefits from cloud transformation. With Generative and Agentic AI, we are following a similar pattern and running into the same issues yet again.

Mastering agentic orchestration requires new strategic playbooks

When evaluating agentic orchestration, fundamentally, organizations must shift the lens from capabilities to outcomes. They can only do that by cutting through the market noise, as most transformations stall without a clear articulation of business goals. Agentic AI alone doesn’t deliver value unless it is operationalized against cost, productivity, and growth objectives. Therefore, start every transformation by setting quantifiable outcome targets before funding tech adoption. The four key pillars of agentic orchestration, as Figure 1outlines, are the central elements of the strategic playbook that comprises the following goals:

Figure 1: The Agentic Orchestration Playbook

• Pivot towards business outcomes and value capture: Structured goal decomposition is central to unlocking value at scale. Those goals must be aligned with clearly articulated business objectives. Agentic orchestration is inherently non-deterministic; therefore, outcomes must drive orchestration rules rather than hardcoding tasks. Set quantifiable outcomes targets before funding technology adoption. Then map your orchestration rules and KPIs directly to those targets – not the other way around.
• Re-architect work, not just technology: Agentic orchestration isn’t just an IT problem; it is a workforce transformation. The foundation is a composable architecture geared toward reuse while avoiding fragmentation. Thus, reimagine roles: expect fewer repetitive jobs but new job roles like agent supervisors, orchestration engineers, and governance specialists. Most importantly, invest in change management early. Without it, adoption stalls.
• Design adaptive, business outcome-centric governance: The strategic goal is to balance agent autonomy and human oversight to enable cross-functional collaboration between engineering, operations, and compliance teams. It is crucial to define agent lifecycle ownership: onboarding, monitoring, retraining, and retirement. Furthermore, create cross-functional orchestration councils. Unsurprisingly, the demand for reskilling of talent will be massive.
• Prepare now for autonomous delivery: While we do not yet have the technologies to enable fully autonomous, end-to-end process delivery, operations leaders must invest in guardrails to prepare for a fundamentally different way of collaboration. These should include incorporating deterministic fallback layers for critical decision points. At the same time, it is essential to track cost transparency, agent decision logs, and error escalation patterns.

Despite the teething problems that reflect the nascent state of deployment, agentic orchestration is the next secular shift, but technology alone won’t get us there. We have been here before with RPA and cloud: too many POCs, too little value capture. The winners in agentic orchestration will not be the enterprises deploying the most agents, but those embedding orchestration into redesigned operating models.