What Was Good For Agents In 2007, Is Good For Agents In 2025!

The following discussion from yesterday’s Post #1 – What does Human – AI harmonization mean in 2025? – https://lnkd.in/g_kymKKj is worth further discussion.

It is the perfect example of what happens when Microsoft‘s Satya Nadella‘s “SaaS death” meets the Hansen Fit Score.

Why Is What Was Valid For Agents In 2007 Still Valid For Agents in 2025?

Here are the core principles from the Hansen Metaprise, Agent-based, and Strand Commonality models that have remained constant from their inception to today, even as technology and organizational structures have rapidly evolved:

1. Alignment Across All Stakeholders (“Strand Commonality”)

• The foundational idea is that true digital and operational success depends on achieving deep semantic, operational, and intent alignment across every “strand” in the enterprise web: buyers, suppliers, partners, practitioners, systems, and regulators.
• The goal is always persistent “echo”—ensuring that the meaning, goals, and requirements set at one end are received, understood, and acted upon consistently across all nodes, preventing drift, miscommunication, or fragmentation.

2. Agent-Based, Decentralized Orchestration

• Both early and current models reject top-down, monolithic command-and-control architectures in favor of distributed, autonomous agents—whether human, digital, or hybrid—empowered to make context-sensitive decisions.
• Agents operate independently, but within a structured, shared logic—able to self-adapt, collaborate, and resolve issues without constant central intervention.

3. Continuous, Living Fit and Feedback Loops

• A core constant is the focus on ongoing, real-time monitoring, recalibration, and feedback—not just snapshot or periodic reviews.
• Systems and agents are expected to continuously check their alignment (“fit”) with the evolving needs, policies, and context of the organization, its partners, and external constraints.

4. Semantic Mapping and Taxonomy Stewardship

• The models have always stressed the criticality of shared language, data definitions, and taxonomic governance.
• Avoiding (and correcting) “synonym drift” or taxonomy misalignment is foundational for sustaining commonality and system health, regardless of platform or technology wave.

5. Human Oversight and Governance as Enduring Components

• Even as agent autonomy and AI Advance, these models have always embedded structured paths for escalation, exception management, audit, and human judgment within automated workflows.
• Governance is never an afterthought: agents are designed to support and extend human expertise, not replace oversight, especially in regulated or high-risk domains.

6. Adaptability to Change and Operational Context

• From the start, the Hansen models call for systems and agents that are resilient and adaptive to external volatility, stakeholder shifts, and local context—not rigid, brittle, or one-size-fits-all.
• This adaptability ensures longevity, continual relevance, and the ability to support both incremental improvement and disruptive change as needed.

“While technology has evolved from simple workflow automation (2007) to adaptive, AI-driven agentic platforms (2025), the core principles—network-wide alignment, agent-based orchestration, continuous fit feedback, semantic stewardship, built-in human governance, and enduring adaptability—have remained the bedrock of the Hansen Metaprise, Agent-based, and Strand Commonality models. These enduring foundations ensure that as technology advances, operational success is still governed by alignment, meaning, transparency, and real-world business context.”

What Are The Differences Between 2007 and 2025 Agents?

The difference between a 2007 Agent and a 2025 Agent in the context of the Hansen Metaprise, Agent-based, and Strand Commonality models is profound—reflecting more than just technological evolution, but a transformation in how agents operate, learn, and align across the enterprise and supply network.

1. Technical Capability & Intelligence

2007 Agent:

• Rule-based and deterministic: Agents in 2007 were primarily workflow bots, process engines, or automation scripts operating within defined boundaries—hard-coded to follow business logic or execute pre-set instructions.
• Limited learning/adaptation: Any learning was primarily algorithmic and required human intervention or reprogramming rather than self-initiated adaptation. Agents had little “context-awareness” beyond their immediate transaction.
• Integration was siloed: Agents typically worked within a single system or enterprise border (e.g., enterprise ERP, procurement portal), with interfaces to other agents or systems handled through brittle APIs or file transfers.

2025 Agent:

• AI-driven, context-aware, and adaptive: Modern agents leverage advanced AI (including LLMs, reinforcement learning, and autonomous orchestration). They continuously learn, interpret intent, and update their logic in real time based on environmental inputs.
• Semantic understanding: 2025 Agents can understand, negotiate, and align meaning (semantic mapping), reducing synonym drift or misinterpretation between humans, systems, and other agents.
• Agentic collaboration: They work in swarms or networks—communicating, trading, and aligning goals not just within the enterprise, but across suppliers, logistics, and even regulatory systems (the true “Metaprise” vision).

2. Strand Commonality and Cross-Stakeholder Echo

2007 Agent:

• Siloed resonance: Commonality—alignment of meaning, intent, and logic—was managed at project or system boundaries, often “force fit” through manual taxonomy mapping or governance meetings. Agents required substantial human oversight to avoid drift and misunderstanding.
• Limited feedback: Feedback loops existed, but were slow, batch-based, or required escalation to people. Adaptation to new terminology, exceptions, or business rules was typically “out of band.”

2025 Agent:

• Self-sustaining echo: Agents now operate with built-in strand commonality checks—they can sense when alignment between different stakeholders, departments, or even external partners is breaking down and initiate recalibration (or escalate for quick human intervention).
• Continuous, multi-tier feedback: Feedback is instant, continuous, and built across every layer—if a supplier or regulator updates requirements, agents can “hear” and propagate these changes across the entire agent network, minimizing misfit and error propagation.

3. Governance, Compliance, and Human Oversight

2007 Agent:

• Governance outside the loop: Risk, compliance, or exception management was almost entirely handled by separate systems or human review; agents could not “see” policy conflicts or new regulatory boundaries except through hard-coded decision points.
• Opaque behavior: Explaining why an agent made a decision was often difficult, limiting trust and adoption in regulated sectors.

2025 Agent:

• Embedded governance: Policy, audit, legal carve-outs, and human escalation paths are explicitly modeled and monitored. Agents can provide explainability and traceability (“why did I make this decision?”) as a core feature.
• Dynamic oversight: Agents can escalate exceptions, learn new rules, and even suggest governance updates in real time—bridging the gap between machine autonomy and human accountability.

4. Operational Impact and Value Realization

2007 Agent:

• Efficiency driver: Focused on cost/practical automation (reducing manual steps, accelerating workflow within the enterprise), but with limited support for business agility, multi-party collaboration, or non-linear value creation.
• Static fit: Agents often went out of alignment as business, supplier, or regulatory requirements evolved—leading to “silent failures” or the need for expensive remediation.

2025 Agent:

• Ecosystem optimizer: Moves from executing tasks to optimizing outcomes across a living network (Metaprise), supporting ongoing business model adaptation, risk management, and cross-boundary value realization.
• Living fit and strand resonance: Agents constantly monitor and recalibrate their “fit” with human, business, and regulatory needs—delivering sustainable, aligned, and transparent value over time.

In Summary

• A 2007 Agent is a rule-bound, deterministic automation tool—helpful, but essentially siloed, brittle, and reliant on manual management for alignment, compliance, and evolution.
• A 2025 Agent is an adaptive, autonomous, semantically aware, and governance-embedded digital actor, capable of real-time learning, ecosystem-wide collaboration, and persistent strand commonality across human and machine stakeholders.

This leap—from static automation to adaptive, agent-based fit—is exactly what the Hansen Metaprise, Agent-based, and Strand Commonality models have long anticipated and are now being realized in leading-edge procurement and enterprise technology.

TODAY’S TAKEAWAY

While technology has and will always continue to evolve, the core or foundational framework of the Hansen Metaprise, Agent-based, Strand Commonality remains constant.

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BONUS QUESTION

What is the difference between the “Human-In-The-Loop” and “Human-At-The-Center” concept?