The Evolution of LLMs Into PALMs
Why Private Agentic Language Models Represent the Next Architecture of Enterprise AI
For the past several years, large language models (LLMs) have dominated the narrative of artificial intelligence. Their sudden leap in fluency, reasoning, and generative capability created a global inflection point, unlocking experimentation across every industry. Yet as enterprises move from pilots to production, a growing realisation is emerging: LLMs alone are not the final architecture of enterprise AI.
They are a foundation — but not the system.
The next phase of AI is not defined by bigger models or better prompts. It is defined by agentic systems, private data, and human-native interfaces. This evolution is giving rise to a new category we call Private Agentic Language Models (PALMs).
This article explores why LLMs are evolving into PALMs, how agentic architectures such as McKinsey’s agentic mesh underpin this shift, and why Human APIs will ultimately replace prompt-driven interaction as the dominant interface for AI at scale.
LLMs Were a Breakthrough — and a Bottleneck
LLMs solved a critical problem: they generalised intelligence. For the first time, a single model could write, reason, summarise, translate, and converse at a level that felt broadly human. This unlocked developer creativity and accelerated AI adoption faster than any previous technology wave.
However, enterprises quickly encountered structural limitations:
LLMs are generalists, not specialists
They are centrally trained, not enterprise-owned
They are stateless, unless wrapped in additional systems
They rely heavily on prompt engineering, which does not scale organisationally
They struggle with governance, auditability, and data sovereignty
In short, LLMs are powerful engines — but engines alone do not make a vehicle.
What enterprises actually need are systems that act, not just models that respond.
The Shift From Models to Systems
As AI moves into core business processes, the unit of value is no longer a response — it is an outcome.
This requires systems that can:
Ingest and reason over private, unstructured data
Maintain context across time and tasks
Coordinate multiple capabilities
Operate securely inside enterprise boundaries
Interact naturally with humans and systems
This is where agentic architectures emerge.
McKinsey captures this transition clearly in its description of the agentic mesh, noting that the future of AI lies in networks of specialised agents that collaborate dynamically rather than relying on a single monolithic model.
As McKinsey describes it, agentic systems move beyond “single-prompt interactions” toward persistent, goal-driven agents that perceive, decide, and act across workflows.
This framing is critical — because it reframes AI from a tool into an operating layer.
From Agentic Mesh to PALMs
The agentic mesh provides the conceptual architecture. PALMs operationalise it in production.
A Private Agentic Language Model is not a single model. It is a system of agents, trained, orchestrated, and governed around an organisation’s proprietary data, processes, and security constraints.
PALMs differ from public LLMs in fundamental ways:
Where LLMs generate language, PALMs generate action.
Why Unstructured Data Is the Real Moat
Enterprises do not lack data. They lack usable intelligence.
The vast majority of enterprise knowledge lives in unstructured formats:
Emails
Documents
Tickets
Logs
Messages
Contracts
Call transcripts
Traditional AI pipelines struggle here. Even many RAG implementations treat unstructured data as passive retrieval stores rather than active substrates for learning and reasoning.
PALMs invert this approach.
They treat unstructured data as:
A living memory
A training signal
A behavioural constraint
A source of institutional knowledge
This is why secure ingestion, agentic RAG, and agentic training are foundational. Without them, agentic systems collapse back into prompt wrappers.
Why Model Agnosticism Is Non-Negotiable
One of the most underappreciated realities of the current AI market is model commoditisation.
New foundation models are emerging at an unprecedented pace — across regions, regulatory regimes, and compute architectures. Enterprises that tie their intelligence layer to a single model vendor are assuming long-term risk.
PALMs are designed to be model-agnostic by default:
OpenAI today, another model tomorrow
Specialised models for specialised agents
Continuous optimisation without replatforming
This aligns directly with McKinsey’s agentic mesh view: intelligence emerges from coordination, not from a single “best” model.
Cloud and Platform Neutrality as Strategic Advantage
Hyperscalers are incentivised to vertically integrate:
Model
Cloud
Tooling
Interface
This creates speed — but also lock-in.
PALMs take the opposite stance. They are:
Cloud-agnostic (AWS, Azure, Google)
Tooling-agnostic
Interface-agnostic
This neutrality matters deeply for:
Governments
Regulated industries
Global enterprises
Long-term risk management
It also aligns incentives. PALMs exist to optimise enterprise outcomes — not cloud consumption.
Human API: The End of Prompting
Perhaps the most important — and least discussed — evolution in AI is how humans interact with it.
Prompting was a bridge technology. It helped us communicate with early models, but it is fundamentally unnatural and exclusionary. Organisations cannot expect entire workforces — let alone customers — to become prompt engineers.
The next interface is not better prompts. It is human-native interaction.
Enter the Human API.
Human API is an interface layer that connects PALMs directly to:
Phone calls
SMS
Email
System messages
Chat environments (ChatGPT-style, Claude, Copilot)
Chat remains important — but it is not the destination. It is the transition.
In collaboration with Nvidia, the development of duplex models signals a critical shift: AI systems that can engage in real-time, two-way interaction where prompting becomes obsolete.
Humans ask questions. Systems respond, act, clarify, and continue.
This is not conversational AI. It is collaborative intelligence.
Why Adoption Happens Through Human Interfaces
Enterprise uptake is not driven by novelty. It is driven by fit.
Human API works because:
It meets users where they already are
It does not require retraining behaviour
It integrates with existing communication flows
It removes cognitive overhead
McKinsey notes that the biggest value creation from agentic systems occurs when they are embedded directly into workflows, not accessed as standalone tools. Human API is the interface manifestation of that principle.
PALMs as the Control Plane for Enterprise AI
When viewed holistically, PALMs function as a control plane:
They orchestrate agents
Govern data access
Route intelligence across models
Interface with humans and systems
Learn continuously within private boundaries
This is why PALMs represent a category shift, not an incremental upgrade.
LLMs answered the question: Can machines understand language?
PALMs answer the question: Can machines operate responsibly inside human systems?
The Market Opportunity
The largest AI value pool does not sit in consumer chatbots or public APIs. It sits in:
Governments
Energy
Finance
Healthcare
Infrastructure
Defence
Enterprise operations
These organisations require:
Security
Sovereignty
Auditability
Longevity
PALMs align structurally with these requirements. Public LLMs do not.
As McKinsey observes, agentic systems represent one of the most significant productivity opportunities since the internet — but only if they are deployed responsibly, securely, and at scale.
Why This Is the Natural Evolution
Technological evolution follows a familiar pattern:
Breakthrough capability
Overgeneralisation
Systemisation
Infrastructure consolidation
LLMs were the breakthrough. Agentic meshes describe the system. PALMs deliver the infrastructure.
This is not a rejection of LLMs. It is their maturation.
Conclusion: From Intelligence to Agency
The future of AI is not about talking to machines. It is about working with them.
Private Agentic Language Models represent the convergence of:
Agentic architectures
Private data
Model neutrality
Human-native interfaces
They are the logical endpoint of the current AI trajectory — and the starting point of enterprise-scale, trustworthy, agentic intelligence.
The organisations that understand this transition early will not just deploy AI faster.
They will deploy it correctly.
And that will define the next decade of competitive advantage.