LAMs vs. Agentic Frameworks: What Actually Works in 2026

If you are a CTO or technical leader trying to figure out where to place your bets in 2026, you have likely encountered a confusing debate. On one side, you hear about Large Action Models, AI systems that can read screens and take actions directly. On the other, you see a growing ecosystem of agentic frameworks that combine language models with orchestration, tools, and memory to accomplish tasks.

The marketing materials from vendors make both approaches sound like the future. But when you dig into what actually works in production today, a clearer picture emerges.

Here is what you need to understand about the LAM versus agentic frameworks debate, and how to make architectural decisions that will serve your enterprise through 2026 and beyond.

The Promise of Large Action Models

Large Action Models generated significant excitement when they first appeared on the scene. The concept was compelling: instead of an AI that just generates text, why not build one that can directly manipulate software interfaces, clicking buttons, filling forms, navigating applications just like a human would?

Adept AI, co-founded by Ashish Vaswani who co-authored the seminal Google paper on transformers, launched ACT-1 with exactly this vision. Salesforce has introduced a family of xLAM models. And Microsoft has demonstrated "computer use" capabilities within its agent frameworks that preview LAM-type functionality .

The idea is seductive. A true LAM would understand not just language but the visual and structural elements of software. It would learn from observing human interactions. It would take actions across any application without requiring custom integrations.

But here is the reality check from enterprises that have tried to deploy them.

What LAMs Actually Deliver Today

Patrick Anderson, managing director at digital consultancy Protiviti, has been watching this space closely. His assessment is measured: "The current players have made good progress toward mimicking what an LAM ultimately seeks to do, but they lack contextual awareness, memory systems and training built into a model of user behavior at an OS level" .

In plain language: today's LAMs can imitate actions, but they do not truly understand what they are doing. They cannot remember why they took certain steps. They cannot learn from mistakes and avoid repeating them.

Vitor Avancini, CTO at AI and data consultancy Indicium, adds another concern. "LAMs, in their current iteration, also carry higher risks. Generating text is one thing. Triggering actions in the physical world introduces real-world safety constraints. That alone slows enterprise adoption" .

There is also a misconception problem. Anderson notes that many vendor offerings that appear to be LAMs are actually "simply combining LLMs with automation" . Microsoft's Copilot and Google's Gemini are powerful tools, but they are not true action models in the sense the research community envisions.

The result is market confusion. Vendors talk about LAM capabilities, but the actual technology remains largely in the research stage .

The Genesys Counter-Example

To be fair, not everyone is waiting. In February 2026, Genesys announced what it calls the industry's first agentic virtual agent powered by large action models for enterprise customer experience .

The Genesys Cloud Agentic Virtual Agent, built in partnership with Scaled Cognition using their APT-1 LAM, is designed to understand customer intent and execute complex actions across front- and back-office systems without human intervention .

Early adopters include M&T Bank, Banco Pichincha, and several Fortune 500 healthcare and retail organizations . The pitch is straightforward: move beyond conversational bots that answer questions to autonomous systems that actually resolve customer requests end-to-end.

Dan Roth, CEO of Scaled Cognition, frames the value bluntly: "In the enterprise, 80% accurate is 100% useless for automation. The foundation of trustworthy automation is super-reliability, not super-intelligence" .

This is a significant claim. If Genesys and Scaled Cognition have cracked the code on reliable, deterministic action execution, it changes the calculus.

But even here, the architecture matters. Genesys is not deploying LAMs in isolation. They are pairing them with "enterprise-grade orchestration and governance" , embedded guardrails, unified data access, transparent decision paths, and support for open standards like Agent-to-Agent (A2A) and Model Context Protocol (MCP) .

In other words, the LAM is a component within a broader agentic system, not a replacement for one.

The Agentic Frameworks Alternative

While LAMs chase the dream of end-to-end action models, agentic frameworks have been quietly delivering value in production.

These approaches start with language models and build around them: planning layers, memory systems, tool integrations, and orchestration logic. The result is systems that can reason through complex workflows, adapt when conditions change, and coordinate with other specialized agents .

According to Avancini, "agentic systems are further along. They don't have the physical capabilities of LAMs, but they already outperform traditional rules-based systems in versatility and adaptability".

The key insight is that you do not need a single model that does everything. You need a system that combines models with reliable infrastructure.

Sreenivas Vemulapalli, senior vice president and chief architect of enterprise AI at Bridgenext, predicts that in 2026, "many enterprises will view manual orchestration as a waste of resources" . Vendors are creating "agentic primitives", standardized building blocks, as commodity offerings in AI platforms .

Derek Ashmore, agentic AI enablement principal at Asperitas, puts it even more directly. "The smart move is to treat low-level agent orchestration as a temporary advantage, not a permanent asset" . He estimates that 10 to 20 percent of leading firms are building internal agent platforms because off-the-shelf tools do not yet provide the reliability they need. But they are doing so knowing that much of this work will soon be commoditized .

The Architecture Decision

So which approach wins for enterprise automation in 2026?

The answer depends on what you are trying to automate.

For tasks that require navigating unpredictable visual interfaces, legacy systems without APIs, applications designed only for human interaction, LAMs may eventually be the answer. Being able to "see" a screen and click the right button without custom integration is genuinely valuable.

But for most enterprise workflows, the systems you need to interact with have APIs. They have structured data. They have defined integration points. In these environments, agentic frameworks built on language models with tool access are already production-ready.

The real differentiator is not whether you use a LAM or an agentic framework. It is whether you have the governance, orchestration, and reliability layers that make autonomous action trustworthy.

Anderson's advice is worth repeating: "As we move toward ambient agents that are autonomous, this will introduce significant risk due to data quality leading to poor decisions" . Without addressing data quality, governance, and observability, neither approach will succeed at scale.

What to Build and What to Buy

If you are a technical leader making decisions today, here is practical guidance drawn from organizations that have been through this.

First, recognize that most of the "LAM" capabilities you see marketed are not true action models. They are language models combined with automation frameworks. That is okay. The combination works.

Second, invest in what makes your business unique. Vemulapalli recommends focusing on "high-quality domain knowledge and ontologies, golden data sets and evaluation suites, security and governance policies, integration into your existing SDLC and SOC workflows" .

The agent engine itself, whether LAM-based or framework-based, will become replaceable. As Ashmore puts it, "use it now to learn what works, but architect your stack so you can swap in vendor innovations as they mature" .

Third, build for observability from day one. The Genesys approach of embedding governance and transparent decision paths is not optional. It is the only way you will gain the confidence to let agents act autonomously.

Fourth, watch the standards. Support for A2A and MCP matters because it ensures your agents can communicate with other agents and systems regardless of vendor. The Genesys commitment to these open standards is a sign of where the industry is heading .

The Bottom Line

The LAM versus agentic frameworks debate is, in some ways, a distraction. What matters is not whether your model can "act" directly, but whether your system can act reliably.

For 2026, the production reality is that agentic frameworks are delivering value today. They combine language models with orchestration, memory, and governance to automate complex workflows. LAMs are promising, but they remain largely in research or early pilot stages, with the notable exception of specialized implementations like Genesys, which still rely on the same governance infrastructure that makes agentic systems work.

Avancini sums it up well: "With the right orchestration, tools and safeguards, agent-based automation is becoming a powerful platform long before LAMs reach mainstream viability" .

The question is not which architecture wins. It is whether your organization has the governance, data quality, and orchestration capabilities to make either one work at scale.