Stop Buying AI Hype and Better Prepare for True Agentic AI Investment

Summary

CRM and Contact Center as a Service (CCaaS) vendors are aggressively positioning their AI platforms as “agentic”, claiming systems that can autonomously reason, adapt, and act without being programmed for every scenario. While platforms have evolved from rigid rules-based systems to more flexible workflows, most still operate deterministically. As a result a discrepancy between marketing and technical reality is widening, creating a credibility problem in enterprise AI procurement. Agentic AI capability must be validated through clearer agentic AI definition, behavioral testing, and buyer-side skill development. This research note outlines what’s required to help buyers cut through the noise and hold vendors accountable for the value true agentic AI should be delivering.

Key Take: Too many “AI-enabled” CCaaS and CRM applications are deterministic systems masquerading as autonomous agents. To avoid buyer regret, enterprises must be clear on what agentic AI means, use real-world edge-case testing, and improve internal skills to identify true agentic behaviors.

Vendors Are Rebranding Automation as Agentic AI to Gain Competitive Edge Causing Confusion for Buyers

The enterprise software landscape is undergoing a paradigm shift, especially in the CRM and CCaaS markets. In an effort to stand out vendors are rushing to label their AI platforms as “agentic”, implying a level of autonomy and adaptability that most current systems have yet to achieve. End-users are challenged with navigating this while trying to ramp up their own AI competency.

• Vendors are aggressively marketing “agentic AI” as a differentiator. AI branding has escalated from “automated” to “intelligent” to “agentic” in the past two years, particularly in the CRM and CCaaS space. Leading platforms now claim capabilities such as self-directed decision-making, adaptive routing, and autonomous service resolution. However, most systems still depend on deterministic flows (predefined, rules-based logic with fixed outcomes) and lack the ability to interpret unstructured inputs or adapt their reasoning in real-time. The gap between promise and capability is expanding as vendors chase market position rather than maturity, setting the stage for widespread buyer disappointment.
• Vendors favor pre-recorded demos often concealing deterministic behavior behind scripted storytelling. Demos are typically stitched together by teams to craft a narrative often leading to subtle inconsistencies that wouldn’t even be present if just Generative AI (GenAI). Mismatched naming, casing, or spelling, suggest pre-programmed outputs rather than live decision-making. Other giveaways include awkward flow dynamics, timestamp mismatches, repetitive data use, inconsistent response formats, or input expectations shifting without cause, (even inconsistencies should be consistently inconsistent). The result is a jigsaw of capabilities that look coherent, even to some experts, but often point to a lack of true agentic behavior.
• Inconsistency in true agency definition means buyers are at risk of buying hype over function. As with all markets there is a lack of knowledge and experience to support buying in the early stages of its lifecycle. End-user companies, especially those outside of AI-native industries, lack the exposure to agentic capabilities to evaluate whether a solution truly offers agentic benefits. This gap in understanding can be exploited by vendors using vague or inflated terminology. The issue is compounded when RFP processes rely on surface-level feature checklists rather than empirical testing or proofs-of-concept tied to real-world complexity.

Sacrificing Storytelling to Show True Agency Leads to Diminishing Returns

Vendors are under pressure to articulate agentic AI in a way that aligns with the technology they are selling. The concept of agency is being framed through characteristics that appear agentic and match what vendors can demonstrate. It has become a race, and the stakes could not be higher. Securing the role of central AI solution within an enterprise is perhaps the most important position a software vendor can achieve, since all other investment decisions will be secondary to the core AI-enabled “orchestration engine.” In many cases, demonstrating true agentic capabilities is sacrificed for the sake of telling a compelling story that ensures relevance. Unless this dynamic is addressed, the gap between expectations and real-world possibility will continue to widen.

• Agentic AI is fundamentally difficult to build while vendors are under pressure to develop faster. True agentic AI requires not only advanced machine learning models, but also orchestration frameworks capable of chaining together multiple tools, maintaining state, and reasoning across incomplete or ambiguous data. These capabilities are not trivial to develop or deploy; they demand large volumes of high-quality training data, rigorous evaluation methods, and often, the integration of multiple sub-systems like planning agents, memory architectures, and retrieval-augmented generation. The pace of the market, accelerated by competitive pressure, is forcing vendors to declare agentic readiness long before it’s achieved.
• Vendors struggle to demonstrate agentic AI, making overclaiming almost inevitable. True agency is difficult to demonstrate in ways that cannot be replicated by deterministic capabilities. Agentic behavior, such as goal-driven planning, tool selection, and decision-making under uncertainty, emerges from reasoning chains, contextual adjustments, and prioritization in the face of incomplete or conflicting data. Demo environments typically lack access to the enterprise data, systems, and workflows required to showcase these capabilities authentically. Even leading agentic AI companies acknowledge that robust agency involves emergent behavior, which is inherently unpredictable and difficult to present effectively in short sales cycles. As a result, marketing teams often rely on narrative stitching and scripted flows to simulate intelligence and present it as autonomy. This normalizes performative AI, erodes trust across the ecosystem, and delays the realization of value that genuine agency could deliver.
• Opacity in messaging makes due diligence nearly impossible. Vendors frequently use ambiguous language to describe capabilities, relying on terms like “intelligent” or “self-learning” or “understands context” without describing what this actually means or how it specifically translates to agentic AI capabilities. Else, vendors may reference uniqueness derived from having large proprietary datasets that increase accuracy (data washing). This isn’t necessarily to purposefully deceive buyers, even capable staff often lack the expertise to describe AI capabilities well enough, themselves. In such a scenario, there’s a risk staff members may quickly find themselves diluting messaging or worse, in an indefensible position when being challenged about real-world agentic AI capabilities. It makes sense to be sufficiently vague and to defer to the “experts” when buyers show intent to buy.

Establish Evaluation Rigor Now to Avoid Disappointment in Agentic AI Investments

To avoid investment mistakes in over-promised tools, end-user companies need to assess agentic AI claims rigorously using structured evaluation methods rather than relying on vendor definitions. Apply a standardized assessment framework, validate capabilities through proofs of concept that expose ambiguity, and upskill staff to recognize genuine behaviors that ensure agentic AI investments are better aligned to enterprise goals.

• Use Actionary’s Agentic AI Assessment Framework for a standardized definition of agentic AI in enterprise software. Our practical evaluation tool assists procurement and technology teams assessing CRM and CCaaS AI capabilities. It provides a clear list of characteristics that demystify what’s truly agentic, (see “Appendix: Vendor Assessment Framework for Agentic AI” for associated guidance). It will help to ensure vendors explain, in concrete terms, how its system behaves when it isn’t told exactly what to do.
• Build proof-of-concept tests that simulate ambiguity and edge cases. Agentic AI introduces a new kind of capability, and therefore requires a new kind of test. A Proof of Concept (PoC) designed specifically to validate true agentic behavior is the only reliable way to justify investment decisions in today’s uncertain AI market — no matter how advanced the demo, it simply can’t reveal whether a system is reasoning on its own. Unlike conventional AI PoCs that assess performance on predefined tasks with clean inputs, agentic AI PoCs must recreate the messiness of the real world: ambiguous goals, broken APIs, shifting objectives, and incomplete or multi-intent inputs. Does the system interpret goals independently, plan multi-step actions, adapt in real time, invoke relevant context, and revise its own strategy? Can it identify missing data, ask clarifying questions, select the right tool, and adjust its behavior across interactions? An agentic AI solution doesn’t follow a fixed path, it navigates uncertainty, reasons through trade-offs, and learns from outcomes. That’s why instrumentation must go deeper: trace decision paths, monitor memory retrieval, capture fallback logic, and observe how it updates its approach. Success is more than task completion, it’s how intelligently and independently the system got there. And this kind of verification can only happen when PoCs are deliberately engineered to expose reasoning and resilience, not just functionality. Getting this right is how organizations separate real agency from scripted automation, and avoid the number one blocker in enterprise AI deployment today: wasting cycles in PoC purgatory. Reference the Appendix to rationalise the list of vendors to engage with on a PoC.
• Build organizational skills to recognize and evaluate agentic behavior. Procurement, product, and engineering teams must be skilled in critically assessing what agentic AI looks like in practice. Most enterprise buyers haven’t been given the opportunity to develop expertise, internally, to be able to differentiate between automated workflows, traditional AI, and true agentic systems. Without a foundational understanding of how agentic AI behaves—goal orientation, tool use, planning, contextual awareness—teams risk misinterpreting deterministic automation as autonomy. Upskilling should focus on three core areas: (1) conceptual literacy about agentic AI properties (e.g., memory, chaining, self-directed decision-making), (2) technical validation skills such as prompt tracing, sandboxing, and structured PoC execution, and (3) cross-functional collaboration between business units and technical teams. Training should also cover how to design evaluation prompts and simulate edge cases to expose agentic limitations or false claims. Finally, organizations should maintain a shared playbook of evaluation methods and patterns learned from real PoCs to institutionalize this knowledge across future procurement cycles.

Appendix: Vendor Assessment Framework for Agentic AI 

The following tables provide a practical evaluation tool for procurement and technology teams assessing CRM and CCaaS AI capabilities. 

Section 1: Core Capabilities of Agentic AI

Section 2: Technical Architecture Transparency

Section 3: Functional Testing & Pilot Design

Section 4: Evaluation Criteria Checklist for Procurement Teams

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