When Agents Go to Work

Theory-Practice Synthesis: February 21, 2026 - When Agents Go to Work

The Moment

We're standing at a peculiar inflection point in February 2026—one that most organizations won't recognize until they've already crossed it. Across enterprise environments, AI agents are quietly transitioning from experimental pilots to production workloads. Heathrow Airport's "Hallie" answers passenger questions about gate locations and security wait times. Safari365 automates $30,000 custom safari itineraries across 3,000 supplier relationships. DeVry University deploys student portal agents that understand course histories and recommend personalized academic pathways.

These aren't demos. They're operational systems handling real customer interactions, real money, and real consequences. And the theoretical frameworks we've relied on to understand "AI systems" are fracturing under the weight of this new reality.

Five research papers published in February 2026 illuminate different facets of this transition—from legal infrastructure for AI governance to the software architecture of multi-agent systems to operational frameworks for human-AI hybrid teams. When viewed together with the messy realities of enterprise deployment, they reveal something neither theory nor practice alone could show: we're not just scaling AI capability, we're operationalizing entirely new categories of organizational infrastructure. The choices being made right now—about data architectures, governance platforms, and coordination protocols—will determine whether agentic AI amplifies human capability or becomes another layer of technical debt we spend the next decade unwinding.

The Theoretical Advance

Paper 1: Legal Infrastructure for Transformative AI Governance

*Gillian K. Hadfield (February 2026)*

Hadfield makes a deceptively simple observation: we spend most of our AI governance energy on substance—what rules should constrain AI development—while neglecting infrastructure—the legal and regulatory mechanisms that generate, implement, and enforce those rules. Her contribution isn't another set of principles; it's a proposal for three foundational infrastructure layers:

1. Registration regimes for frontier models: Creating transparency about which models exist, their capabilities, and their deployment contexts

2. Registration and identification systems for autonomous agents: Establishing verifiable identity for AI agents operating in digital and physical spaces

3. Regulatory markets: Designing competitive markets where private companies innovate on regulatory services, licensed by jurisdictions but operating at global scale

The theoretical insight is that governance isn't a constraint to be minimized—it's infrastructure to be built. Just as contract law enabled complex commerce and HTTPS enabled secure e-commerce, AI governance infrastructure could enable safe autonomy at scale.

Paper 2: Governance at the Edge of Architecture: Regulating NeuroAI

*Afifah Kashif and team (February 2026)*

This paper exposes a critical gap: current AI governance frameworks assume static, centrally-trained neural networks running on von Neumann hardware. But neuromorphic computing—brain-inspired architectures using spiking neural networks on specialized hardware like Intel's Loihi—breaks all these assumptions. These systems learn continuously, adapt in real-time, and their "models" are inseparable from the physics of the hardware they run on.

The theoretical contribution is recognizing that governance must co-evolve with architecture. Regulatory benchmarks designed for discrete training runs and fixed model weights become meaningless for systems that learn continuously through embodied interaction. The paper argues for "assurance and audit methods that align with the physics, learning dynamics, and embodied efficiency of brain-inspired computation."

Paper 3: The Evolution of Agentic AI Software Architecture

*Mamdouh Alenezi (February 2026)*

Alenezi provides the most comprehensive technical analysis of the architectural transition from stateless prompt-response loops to goal-directed agentic systems. The paper maps classical intelligent agent theory (reactive control, deliberative planning, Belief-Desire-Intention models) onto contemporary LLM-centric patterns.

Key theoretical contributions include:

- A reference architecture separating cognitive reasoning from control flow, memory, tool execution, and governance

- A taxonomy of multi-agent topologies (orchestrator-worker, router-solver, hierarchical, swarm) with mapped failure modes

- An enterprise hardening checklist linking observability, policy enforcement, and reproducibility to governance pillars

The core insight: agentic behavior is an architectural property, not just model capability. Agency arises from clean separation of concerns—cognition, state management, tool interfaces, policy enforcement—implemented as verifiable contracts rather than ad-hoc scaffolding.

Paper 4: Agentic Reasoning for Large Language Models

*Survey paper by Weitianxin and team (January 2026)*

This comprehensive survey reframes LLMs as autonomous agents capable of planning, acting, and learning through continuous interaction. It organizes agentic reasoning along three layers:

1. Foundational agentic reasoning: Core single-agent capabilities (planning, tool use, search)

2. Self-evolving agentic reasoning: How agents refine capabilities through feedback, memory, adaptation

3. Collective multi-agent reasoning: Coordination, knowledge sharing, collaborative goal pursuit

The theoretical framework distinguishes in-context reasoning (scaling test-time interaction through structured orchestration) from post-training reasoning (optimizing behaviors via reinforcement learning). This distinction matters because it separates capabilities that can be added through better prompting/tooling from those requiring model retraining.

Paper 5: HAIF: Human-AI Integration Framework

*Marc Bara (February 2026)*

Bara addresses the operational gap that existing frameworks (Agile, DevOps, MLOps, AI governance) don't solve: how to organize daily work in teams where AI agents perform substantive, delegated tasks alongside humans. HAIF proposes:

- Protocol-based operational system with four core principles

- Formal delegation decision model distinguishing what AI owns versus assists

- Tiered autonomy with quantifiable transition criteria

- Feedback mechanisms integrating into existing Agile/Kanban workflows

The framework explicitly confronts the adoption paradox: the more capable AI becomes, the harder it is to justify oversight—yet the greater the consequences of not providing it. HAIF's theoretical contribution is recognizing that human-AI teams require entirely new coordination primitives, not just "AI adoption" within existing structures.

The Practice Mirror

Business Parallel 1: Salesforce Agentforce - The Data Substrate Reality

When Heathrow Airport deployed its customer service agent "Hallie," Director Peter Burns was blunt about success factors: "An agentic experience is only as good as the data that drives it." All of Heathrow's customer data lives in Salesforce's Data 360 platform—clean, structured, matched to customers at every journey stage.

Safari365's story is even more revealing. Before implementing Agentforce, they managed $30,000 custom safari trips using spreadsheets and Word documents. When they initially migrated to Salesforce, it took massive effort to rebuild pricing logic for 3,000 suppliers. But when Agentforce launched, founder Marcus Brain said: "Because our data is so clean and structured, we were in a great position... we could immediately take advantage of the automation because all the inputs were already there."

Implementation metrics:

- Heathrow: Data 360 foundation required before agent deployment

- Safari365: 3,000 supplier integrations, complete data cleanup before agentic capabilities activated

- DeVry University: Course history integration required to prevent recommending completed/irrelevant courses

The business parallel confirms Hadfield's infrastructure thesis: governance infrastructure must precede agentic capability. Without Data 360 as a governed substrate, these agents would hallucinate answers based on stale or incorrect information. The infrastructure isn't a nice-to-have—it's the foundation that makes agency possible.

Business Parallel 2: Indeed's Organizational Restructuring

When Indeed deployed agents across job search workflows, VP of Business Automation Linda West identified three factors that distinguish agentic implementations from traditional tech rollouts:

1. Team structure changes: "Agent deployments require fundamentally changing who is involved in building these types of products"

2. Data source enrichment: "The more context the agent has, the more powerful... investing time in understanding what data sources will enrich the context is critical"

3. Human-agent alignment: "You can't underestimate that in most cases, you need humans and agents to work hand in hand"

West's observation about alignment echoes HAIF's delegation protocols. Indeed discovered that clarity with human teams on "the vision and the problem set" was essential—exactly the coordination substrate that Bara's framework formalizes.

Business Parallel 3: The Verification Paradox (ODSC Analysis)

McKinsey analysis shows many companies miss meaningful AI impact because they "apply AI to individual tasks instead of redesigning end-to-end workflows." But the real friction is more insidious: time saved gets offset by revision, verification, and rework.

Harvard Business School field studies reveal AI's "jagged" impact across tasks—it boosts speed on some work while reducing quality on others. Teams swing between "algorithm aversion" after errors and "algorithm appreciation" that creates overreliance. Without protocols for validation standards, escalation paths, and override authority, people either rubber-stamp AI output or ignore it entirely.

Workday reports that rework—rewriting, correcting, verifying—can consume most of the time "saved" by AI automation. One enterprise AI practitioner described it as "false ROI": dashboards show productivity gains, but workers experience increased cognitive load.

Business outcomes:

- National Bureau of Economic Research study: AI improved customer-support throughput, but benefits were heterogeneous based on integration approach

- Pew Research: Many workers feel worried/overwhelmed about AI's workplace impact

- Microsoft data: Professionals worry AI will replace jobs, leading to "shadow AI" adoption that bypasses governance

This practice reality exposes a gap in theoretical frameworks: they present agentic systems as autonomous problem-solvers, underestimating the cognitive load humans bear in validating agent outputs.

Business Parallel 4: Gartner's Billion-Dollar Governance Market

Gartner predicts that by 2030, fragmented AI regulation will extend to 75% of the world's economies, driving a $1+ billion market for AI governance platforms. This isn't speculative—companies are already purchasing governance as infrastructure-as-a-service.

The market emergence validates Hadfield's regulatory markets proposal: governance isn't staying in policy documents, it's being operationalized into purchasable platforms with:

- Registration systems for models and agents

- Policy enforcement layers (RBAC, audit logs, typed tool interfaces)

- Observability infrastructure (tracing, evaluation, cost monitoring)

- Compliance frameworks (SOC 2, HIPAA, GDPR, ISO 27001)

Platforms like TrueFoundry, Kore.ai, and ZenML aren't selling "AI tools"—they're selling governed autonomy. The business case is clear: companies will pay for infrastructure that enables safe agent deployment, not just capability.

The Synthesis

When we view theory and practice together, three patterns emerge that neither alone reveals:

Pattern 1: Infrastructure Precedes Agency

Hadfield's governance infrastructure thesis predicts exactly what Heathrow and Safari365 demonstrate: agent autonomy requires governed substrates. The theoretical principle—that legal and regulatory infrastructure enables complex coordination—maps precisely to the technical reality that Data 360, typed tool interfaces, and policy enforcement layers must exist before agents can act reliably.

This isn't correlation—it's causation. Agents can't "just learn" to be reliable when the data they access is fragmented, stale, or inconsistent. The capability framework must be operationalized first. This pattern has profound implications: organizations investing in "AI agents" without first building governed data infrastructure are building on sand.

Pattern 2: The Verification Paradox Exposes Theoretical Blind Spots

Theory presents agentic reasoning as planning → tool use → adaptation. Practice reveals a different loop: generation → verification → rework → adaptation. The cognitive load of validation isn't an edge case—it's the dominant workflow.

Harvard's finding that AI impact is "jagged" across tasks reveals why: autonomy isn't universal, it's task-dependent and context-dependent. Alenezi's reference architecture separates cognition from control flow, but it doesn't address the human role in that control loop. HAIF begins to fill this gap by formalizing delegation protocols, but the theory is still catching up to practice.

The gap matters because it changes ROI calculus. If verification workload isn't factored into productivity estimates, organizations will systematically overestimate AI benefits and under-invest in the operational infrastructure (training, protocols, escalation paths) needed to make verification sustainable.

Emergence 1: Governance as Infrastructure-as-a-Service

Neither Hadfield's theoretical framework nor Gartner's market projection alone tells the full story. The synthesis reveals something new: governance is becoming a purchasable product, not just a compliance burden.

The convergence of:

- Hadfield's regulatory markets (private companies delivering governance services)

- Gartner's $1B+ platform market projection

- Salesforce's Data 360 architecture (governance as technical substrate)

- TrueFoundry/Kore.ai/ZenML (observability, RBAC, audit logs as platform features)

...shows that governance infrastructure is following the same trajectory as cloud infrastructure. What started as custom-built, organization-specific solutions is standardizing into platforms. Companies that treat governance as product will gain competitive advantage; those treating it as overhead will accumulate technical debt.

This transforms the framing: governance isn't a constraint minimizing risk—it's infrastructure enabling capability. The question isn't "how much governance do we need?" but "which governance infrastructure best enables the autonomy we want?"

Emergence 2: Coordination Protocols Are the Real Bottleneck

Alenezi's multi-agent topologies (orchestrator-worker, hierarchical, swarm) map directly to Indeed's team restructuring and HAIF's delegation protocols. The synthesis reveals: the bottleneck isn't model capability, it's coordination protocols.

Indeed's insight that "humans and agents need to work hand in hand" isn't about collaboration—it's about explicit communication contracts. Alenezi's typed tool interfaces, policy enforcement layers, and escalation triggers are the technical manifestation of what Bara formalizes as delegation decision models.

The coordination substrate includes:

- Message schemas (structured fields vs. free-form natural language)

- Shared memory contracts (global scratchpad vs. per-agent memory with selective sharing)

- Authority models (which agents can commit external actions, when human approval required)

- Validation protocols (gold datasets, spot checks, error budgets)

Without these coordination primitives, multi-agent systems exhibit the failure modes Alenezi catalogs: deadlocks, redundant work, conflicting actions, error amplification. The practice data from Indeed and ODSC shows these aren't theoretical risks—they're operational realities.

Temporal Relevance: Why February 2026 Matters

We're at the inflection point where agentic systems transition from pilots to production. The infrastructure decisions being made this quarter will compound:

1. Platform lock-in is real: Organizations choosing governance platforms now (Salesforce Data 360, TrueFoundry, ZenML) are making multi-year architectural commitments

2. Coordination debt accumulates: Teams deploying agents without explicit protocols (Indeed's lesson) will face escalating rework and verification costs

3. Regulatory complexity is fragmenting: Gartner's prediction of 75% economy coverage means compliance becomes a moving target without infrastructure-as-a-service solutions

The gap between theory (clean agent loops) and practice (messy verification workflows) is creating demand for exactly the frameworks the February 2026 papers propose. HAIF emerged because existing frameworks failed. Hadfield's regulatory markets proposal gains urgency because Gartner identifies a billion-dollar need.

This temporal moment matters because we still have agency (no pun intended). The infrastructure isn't locked in yet. Organizations can choose governed substrates over quick-and-dirty scaffolding. Teams can adopt coordination protocols before accumulating verification debt. Policymakers can design regulatory markets before fragmentation becomes overwhelming.

Six months from now, these choices will be constraints.

Implications

For Builders:

1. Invest in governance infrastructure first, agentic capability second. Data 360, typed tool interfaces, policy enforcement layers aren't nice-to-haves—they're prerequisites. Safari365's experience shows that data cleanup before agent deployment determines success more than model selection.

2. Design for verification from day one. Don't treat validation as an afterthought. Build gold datasets, define escalation triggers, instrument verification workflows. The ODSC finding that rework offsets time saved means verification load is a first-class design constraint.

3. Make coordination protocols explicit. Don't rely on implicit human-agent alignment. Implement HAIF's delegation decision models. Use Alenezi's typed interfaces and authority models. Indeed's lesson is clear: team structure changes and explicit alignment protocols aren't optional.

4. Choose platforms that treat governance as product. Evaluate AI platforms not just on model capability but on RBAC, audit logs, observability, and policy enforcement. The $1B governance market means vendors will compete on infrastructure quality—choose accordingly.

For Decision-Makers:

1. Recognize that governance is infrastructure, not overhead. Hadfield's framing shift matters: legal and regulatory infrastructure enables complex coordination. Budget for governance platforms the same way you budget for cloud infrastructure.

2. Plan for heterogeneous autonomy, not universal capability. Harvard's "jagged" AI impact finding means you can't assume agents will work uniformly well across all tasks. Design delegation boundaries task-by-task, with clear human override paths.

3. Account for verification load in ROI calculations. McKinsey and Workday data show that productivity gains get offset by rework. Don't greenlight agentic deployments based on time-saved metrics alone—measure net outcomes including verification cost.

4. Use this inflection point strategically. Organizations that build governed infrastructure now will have competitive advantage as regulatory fragmentation increases. Those treating agents as tactical tools will accumulate coordination debt and governance risk.

For the Field:

1. Bridge the theory-practice gap on verification. Theoretical frameworks need to incorporate human validation load as a first-class concern. HAIF begins this work, but we need formal models of human-in-the-loop control systems for agentic architectures.

2. Develop coordination protocol standards. The emergence of typed tool interfaces, delegation models, and authority frameworks suggests readiness for standardization. The field should converge on coordination primitives the way web services converged on REST/HTTP.

3. Research neuromorphic governance urgently. Kashif's paper reveals a critical gap: governance frameworks designed for static models will fail for continuously-learning, embodied systems. As neuromorphic computing scales, this gap becomes a crisis.

4. Track governance platform evolution. The $1B market Gartner predicts will shape how agentic systems actually deploy. Academic research should study these platforms empirically—what governance mechanisms work, what creates lock-in, what enables interoperability.

Looking Forward

The most provocative question emerging from this synthesis isn't about AI capability—it's about organizational capability. Can companies build the coordination protocols, governance infrastructure, and verification systems that agentic AI requires?

The optimistic scenario: we learn from Safari365, Heathrow, and Indeed. We operationalize HAIF's delegation models. We purchase governance-as-a-service from the platforms Gartner predicts. We build the infrastructure Hadfield describes. In this future, agentic AI amplifies human capability because we've built coordination substrates that preserve human sovereignty while enabling AI autonomy.

The pessimistic scenario: we rush into agentic deployments without infrastructure. We accumulate verification debt and coordination chaos. We treat governance as overhead rather than product. We discover that "autonomous" agents require more human supervision than the systems they replaced. In this future, AI becomes another layer of technical debt we spend a decade unwinding.

February 2026 is the moment we choose which path to take. The theory tells us what infrastructure we need. The practice shows us what happens without it. The synthesis reveals that governance isn't a constraint on capability—it's the substrate that makes capability possible.

The question isn't whether AI agents will transform work. They already are. The question is whether we'll build the infrastructure to make that transformation sustainable.

Sources

Research Papers:

- Hadfield, G.K. (2026). Legal Infrastructure for Transformative AI Governance. arXiv:2602.01474 [cs.AI]

- Kashif, A. et al. (2026). Governance at the Edge of Architecture: Regulating NeuroAI. arXiv:2602.01503 [cs.ET]

- Alenezi, M. (2026). The Evolution of Agentic AI Software Architecture. arXiv:2602.10479 [cs.AI]

- Weitianxin et al. (2026). Agentic Reasoning for Large Language Models. arXiv:2601.12538 [cs.AI]

- Bara, M. (2026). HAIF: A Human-AI Integration Framework for Hybrid Team Operations. arXiv:2602.07641 [cs.SE]

Business Sources:

- Salesforce. (2026). Salesforce Customers on Deploying Agentforce

- Gartner. (2026). Global AI Regulations Fuel Billion-Dollar Market for AI Governance Platforms

- ODSC. (2026). Managing Human + AI Workflows: The Operating Model Most Teams Are Missing