1. Working with Agentic AI: Enterprise Architect

How human skills and agentic skills are dynamically integrated and coordinated across enterprise architecture responsibilities

1.1. Enterprise Architect as an Occupation

An enterprise architect aligns business strategy with technology infrastructure by designing comprehensive enterprise architecture frameworks, governing solution designs, managing technology portfolios, and guiding digital transformation initiatives. This occupation connects high-level organizational objectives with technical execution by structuring business capabilities, defining technology standards, and applying architectural principles to support scalability, security, and operational efficiency. To ensure these architectural frameworks achieve their intended impact, the architect must understand how business strategy, legacy systems, cloud infrastructure, integration patterns, security compliance, and industry standards collectively determine the effectiveness of the technology landscape.

Typical responsibilities of an enterprise architect include:

• enterprise architecture strategy and vision development;
• business and capability modeling;
• technology landscape and portfolio management;
• solution architecture and design governance;
• cloud and infrastructure architecture;
• data and integration architecture;
• security, risk, and compliance architecture; and
• stakeholder alignment and technical roadmapping.

These responsibilities are fulfilled through distinct tasks and subtasks, each requiring one or more actions with specific skill requirements. Human skills and agentic skills both contribute to these responsibilities, but their levels and forms of involvement differ according to the responsibility, task, action, system complexity, regulatory context, technical environment, and expected outcome.

1.2. Human Skills and Agentic Skills in Enterprise Architecture

Enterprise architecture increasingly integrates both human skills and agentic skills.

Human skills are reusable capabilities possessed and developed by enterprise architects. They include strategic reasoning, business alignment, system thinking, stakeholder negotiation, architectural governance, risk assessment, technology forecasting, abstraction, and structural evaluation.

Agentic skills are reusable capabilities available to AI agents through their underlying models, contextual information, instructions, constraints, tools, and computational resources. In enterprise architecture, these skills include codebase scanning, dependency mapping, technology trend summarization, architecture diagram generation, compliance checking, portfolio inventory extraction, cloud cost calculation, infrastructure-as-code generation, and documentation formatting.

Both skill types contribute to the same occupational responsibilities, but their involvement varies by action. Understanding the strategic direction of the organization relies primarily on human business alignment and system thinking; an AI agent then uses agentic skills to summarize industry technology trends and extract competitive benchmarks that support the architect’s vision. In technology governance, the architect first establishes the architectural principles and integration standards, and an AI agent subsequently scans existing code repositories and maps system dependencies. For final delivery, an AI agent generates compliance reports and infrastructure cost projections, while the architect reviews the final outputs to ensure strategic alignment and operational viability. Across these actions, human and agentic skills are activated sequentially or iteratively based on task requirements, with the human professional maintaining oversight and final judgment.

The relationship between these skills is not a fixed division where certain responsibilities permanently belong to humans and others to AI. Instead, human and agentic involvement shifts across tasks and between different actions within the same task.

1.3. Dynamic Human–Agentic Skill Integration and Collaboration in Enterprise Architecture

Enterprise architecture practice can be understood as an occupation in which responsibilities are fulfilled through tasks and subtasks, tasks and subtasks are executed through actions, and actions require appropriate configurations of human and agentic skills.

An occupation provides the overall professional context. For enterprise architecture, this context encompasses aligning business strategy with IT infrastructure, developing and maintaining architecture frameworks, governing solution designs, and supporting organizational digital transformation.

A responsibility represents an expected area of professional work or an outcome that the enterprise architect accomplishes or maintains. Responsibilities such as capability modeling, design governance, cloud architecture, and technology portfolio management each contain multiple tasks.

A task is a context-dependent unit of work used to fulfill part or all of a responsibility. When a task is too broad or complex for reliable execution, it is decomposed into smaller subtasks.

Tasks and subtasks are executed through one or more actions. Actions represent reusable and configurable operations such as scanning a legacy codebase, mapping API dependencies, generating an infrastructure topology, calculating cloud migration costs, checking a design against security baselines, or drafting an architecture decision record.

Each action has skill requirements. These requirements describe the capabilities needed for effective and reliable execution. Depending on the action and its context, human skills, agentic skills, or an integration of both can satisfy these requirements.

The skills available for enterprise architecture reside in a skill space containing both human skills and agentic skills. Relevant skills in this space serve as candidate skills for executing the actions required by a particular task. A human skill in system thinking, for example, serves as a candidate for defining domain boundaries in a microservices migration, while an agentic skill in dependency mapping serves as a candidate for identifying existing service couplings.

Candidate skills are evaluated relative to the requirements of the action and its occupational and task context. Appropriate skills are then selected and organized into a skill configuration for execution. Depending on these requirements, a configuration relies mainly on human skills, mainly on agentic skills, or on an integrated arrangement of both.

1.3.1. Skills Competition and Skills Collaboration

Two important relationships can occur between human skills and agentic skills when constructing a skill configuration: skills competition and skills collaboration.

Skills competition occurs when both a human skill and an agentic skill can satisfy the same or similar requirements for an action, but simultaneous application of both is unnecessary. Under a particular occupational and task context, one skill type provides a better fit for the action than the other.

For example, both an enterprise architect and an AI agent can generate a standard network topology diagram for a routine office expansion. When the hardware requirements, subnetting rules, and security zones are clearly specified in the enterprise standards, the agentic skill provides instant and consistent execution. When the architecture requires designing a novel, multi-region disaster recovery strategy to mitigate unprecedented geopolitical risks, the architect’s human skills provide the necessary strategic and structural judgment.

The purpose of skills competition is not to establish whether human or agentic skills are generally superior. Rather, it identifies which available skill provides the better fit for a particular action under particular conditions.

Skills collaboration occurs when one skill type alone cannot effectively satisfy the requirements of a task or its actions, or when combining complementary human and agentic skills produces stronger task performance.

Collaboration does not require human and agentic skills to operate simultaneously. Instead, they are activated at different stages of action execution in a sequential, iterative, or recursive manner according to evolving task requirements.

For example, an architect first identifies that a legacy monolithic application requires modernization to support global expansion. An AI agent then scans the existing codebase and maps all internal and external API dependencies. The architect subsequently evaluates the dependency map, determines that a microservices approach hosted on a specific cloud provider is most appropriate, and defines the domain boundaries. Based on the architect’s direction, the agent next generates the initial infrastructure-as-code templates and cloud cost estimates for the proposed domains. Finally, the architect reviews the templates, adjusts the security group configurations to meet enterprise compliance standards, and finalizes the migration architecture. Throughout this sequence, human and agentic skills are activated iteratively as task requirements evolve, while the architect directs the structural process and evaluates its outcomes.

This dynamic relationship allows enterprise architects to leverage the complementary strengths of both skill types rather than relying on a permanent allocation of work.

1.3.2. Skill Configuration and Task Execution

A skill configuration specifies which human skills and agentic skills are used to execute the actions associated with a task or subtask and how those skills are coordinated.

The configuration depends on factors such as action requirements, system complexity, regulatory strictness, technical debt levels, business criticality, available cloud services, integration standards, and intermediate stakeholder feedback.

For one action, an agentic skill independently satisfies the requirements. For another, a human skill provides the better fit. A complex task requires several configurations across multiple actions, with human and agentic skills repeatedly interacting as the task progresses.

The configuration is therefore dynamic rather than permanent. It changes when the action changes, new regulatory mandates become available, an agentic result fails to satisfy security requirements, or human interpretation identifies a more scalable structural direction.

Throughout this process, the enterprise architect remains the professional responsible for directing and controlling the work associated with the occupation and its responsibilities. Agentic AI provides powerful complementary capabilities that expand what the professional can scan, map, calculate, generate, govern, and integrate.

1.3.3. Task Performance Evaluation and Skill Space Adaptation

The performance produced through a selected skill configuration is evaluated against predefined evaluation metrics. These metrics determine whether the human skills, agentic skills, or their integrated configuration are sufficiently capable of performing the relevant task and its required actions.

Evaluation criteria depend on the work being performed. In enterprise architecture, they include strategic alignment, structural scalability, security compliance, cost efficiency, technical debt reduction, integration reliability, system resilience, and stakeholder consensus.

When a selected skill or skill configuration produces performance above the defined threshold, it passes the performance evaluation and is retained for future applications under appropriate conditions. Repeated successful use provides additional evidence about where that skill or configuration performs effectively.

When performance falls below the required threshold, the corresponding skill or configuration is not reused without modification. The relevant human or agentic skills are refined or reconfigured, existing skills are combined differently, or new human and agentic skills are developed or constructed from scratch when the existing skill space lacks adequate capability.

For example, an AI agent first generates architecture decision records (ADRs) based on project meeting transcripts, but repeatedly fails to capture the specific regulatory constraints mandated by the enterprise risk committee. Task performance evaluation identifies this compliance omission as falling below the required threshold. The agentic skill is then refined through stricter compliance prompts, updated risk-framework templates, or access to the enterprise’s centralized regulatory metadata repository. The revised skill is evaluated again, and once it satisfies the required performance criteria, it is retained for appropriate future documentation tasks. Human skills follow a corresponding development process: when an architect lacks the knowledge required to design for a newly adopted quantum-resistant encryption standard, the relevant capability is developed through learning, certification, and practical experimentation before its performance is evaluated through subsequent work.

Refined and newly constructed skills that satisfy the required performance criteria are added to the skill space. In this way, task performance evaluation provides feedback not only for completing current work but also for continuously optimizing and expanding the available skill space.

The skill space is not constructed as an isolated collection of occupation-specific representations. Many human and agentic skills used in enterprise architecture are reusable across occupations. System thinking, dependency mapping, technology trend summarization, cost calculation, compliance checking, diagram generation, and documentation formatting contribute to work in solution architecture, DevOps engineering, IT project management, cybersecurity, business analysis, and many other fields.

Reusable skills can therefore be represented once and configured differently according to the responsibilities, tasks, actions, and contexts of different occupations. This allows the skill space to serve a growing range of occupations without unnecessarily duplicating or exploding the number of skill representations. Occupational specialization is introduced through task context, action requirements, parameters, enterprise constraints, and skill configurations rather than by creating a completely separate skill representation whenever the same underlying capability appears in another occupation.

The resulting process is adaptive:

occupational responsibility → task and subtask → actions → skill requirements → candidate human and agentic skills → skill configuration → task execution → task performance evaluation → skill refinement, reconfiguration, or construction → optimized and expanded skill space

This cycle allows human and agentic capabilities to evolve alongside the requirements of enterprise architecture work.

1.4. Essential Responsibilities of Enterprise Architect

1.4.1. Enterprise Architecture Strategy and Vision Development

Enterprise architecture strategy and vision development establishes the long-term technology direction, core guiding principles, and target-state frameworks required to support the organization’s business objectives. This responsibility includes understanding executive strategy, analyzing industry disruption, defining the architectural vision, and structuring the overarching transformation roadmap.

Agentic skills contribute extensively to market research, technology trend summarization, competitor infrastructure analysis, and strategic theme extraction. Human skills remain essential for aligning technology with unstated business ambitions, interpreting macroeconomic impacts on IT investments, crafting the architectural narrative, and deciding which technological paradigms will yield the highest strategic advantage.

For example, an architect first defines the strategic objectives for a multi-year digital transformation initiative. An AI agent then gathers and summarizes relevant industry benchmarks, competitor technology stacks, and emerging cloud service capabilities. The architect subsequently synthesizes this information to craft a target-state enterprise architecture vision and core guiding principles. Based on the architect’s vision, the agent next generates several potential transition pathways and high-level cost projections. Finally, the architect selects and refines the most viable strategic roadmap to guide the subsequent architectural phases. This sequence combines human strategic alignment and structural judgment with agentic research, summarization, and projection organization.

1.4.2. Business and Capability Modeling

Business and capability modeling translates high-level business strategy into a structured inventory of organizational capabilities, processes, and value streams. This responsibility includes mapping business functions, identifying capability gaps, linking processes to technology enablers, and sequencing capability maturity improvements.

Agentic skills are well suited to process extraction from legacy documentation, capability map formatting, value stream sequencing, and cross-referencing applications to business functions. Human skills contribute to determining the true strategic value of capabilities, recognizing redundant organizational structures, designing future-state digital capabilities, and deciding how to prioritize capability investments.

For example, an AI agent first extracts existing business processes from legacy documentation and proposes a preliminary capability map. The architect reviews the map and determines that several core capabilities are missing or incorrectly grouped due to outdated organizational structures. The architect then restructures the map, defining new digital capabilities required for future omnichannel operations. The agent subsequently populates the revised capability map with linked application portfolios and data entities based on the architect’s new structure. Finally, the architect validates the strategic alignment of the updated capability model and finalizes the business architecture baseline. Human and agentic skills interact iteratively as the model progresses from raw documentation to a structured strategic asset.

1.4.3. Technology Landscape and Portfolio Management

Technology landscape and portfolio management maintains a comprehensive inventory of the enterprise’s hardware, software, and cloud services, ensuring that technology investments deliver value and minimize risk. This responsibility includes tracking technology lifecycles, identifying technical debt, rationalizing redundant applications, and enforcing technology standards.

Agentic skills efficiently scan production environments, identify end-of-life components, calculate licensing costs, map application redundancies, and generate technical debt inventories. Human skills are necessary for evaluating the operational risk of legacy systems, negotiating the retirement of entrenched applications, defining the approved technology standards, and applying strategic judgment to portfolio rationalization.

For example, an architect first establishes the evaluation criteria and lifecycle policies for the enterprise technology portfolio. An AI agent then scans all active production environments, identifies end-of-life software components, and generates a comprehensive technical debt inventory. The architect reviews the inventory and determines which legacy systems pose the highest operational risk and require immediate retirement. Based on the architect’s prioritization, the agent next drafts the decommissioning schedules and identifies modern, supported replacement technologies. Finally, the architect approves the portfolio rationalization plan and updates the enterprise technology standards.

1.4.4. Solution Architecture and Design Governance

Solution architecture and design governance ensures that individual project designs align with the broader enterprise architecture, adhere to established standards, and integrate seamlessly with existing systems. This responsibility includes reviewing project proposals, enforcing architectural principles, resolving design conflicts, and maintaining the architecture repository.

Agentic skills support design document scanning, compliance checking against enterprise standards, dependency validation, repository updating, and checklist generation. Human skills are essential for interpreting the intent behind architectural principles, mediating design conflicts between project teams and enterprise standards, and granting formal governance approval.

For example, a project team first submits a proposed solution design for a new customer-facing portal. An AI agent then scans the design documents, checks them against the enterprise architecture principles, and flags violations regarding data residency and approved integration patterns. The architect reviews the flagged violations, mediates a discussion with the project team, and defines the required architectural corrections. The agent subsequently regenerates the compliance checklist and updates the architecture repository to reflect the approved design changes. Finally, the architect grants formal design governance approval, allowing the project to proceed to implementation.

1.4.5. Cloud and Infrastructure Architecture

Cloud and infrastructure architecture designs the foundational compute, network, and storage environments that host enterprise applications, ensuring high availability, scalability, and cost-efficiency. This responsibility includes selecting cloud service models, designing network topologies, defining disaster recovery strategies, and provisioning infrastructure-as-code.

Agentic skills assist with infrastructure-as-code generation, cloud cost calculation, network diagram drafting, region latency estimation, and template formatting. Human skills are especially important for defining the high-availability requirements, balancing the trade-offs between cost and resilience, and ensuring the infrastructure topology supports the overarching business continuity strategy.

For example, an architect first defines the high-availability and disaster recovery requirements for a mission-critical cloud migration. An AI agent then generates multiple infrastructure-as-code templates for different cloud regions and calculates the projected monthly compute and storage costs. The architect reviews the options and identifies that the active-active configuration exceeds the approved budget while the active-passive configuration introduces unacceptable latency. The architect then directs the agent to design a hybrid warm-standby topology that balances cost and recovery time objectives. The agent subsequently produces the updated network diagrams and deployment scripts. Finally, the architect verifies the topology against enterprise resilience standards and authorizes the cloud provisioning.

1.4.6. Data and Integration Architecture

Data and integration architecture defines how information flows, transforms, and persists across the enterprise, ensuring data consistency, accessibility, and semantic accuracy. This responsibility includes designing integration patterns, defining canonical data models, establishing API governance, and selecting message broker topologies.

Agentic skills efficiently map existing point-to-point integrations, identify data duplication bottlenecks, generate API schema definitions, and draft message routing configurations. Human skills are necessary for recognizing the strategic value of specific data domains, designing event-driven paradigms, defining canonical data standards, and ensuring data governance aligns with business semantics.

For example, an AI agent first maps the existing point-to-point API integrations across the enterprise and identifies severe data duplication and latency bottlenecks. The architect reviews the integration landscape and determines that transitioning to an event-driven architecture via a centralized message broker is necessary. The architect then defines the canonical data models and event schema standards for the new broker. Based on the architect’s standards, the agent next generates the message routing configurations and data transformation scripts for the priority domains. Finally, the architect validates the data consistency and governance rules before the new integration pattern is deployed to production.

1.4.7. Security, Risk, and Compliance Architecture

Security, risk, and compliance architecture embeds security controls, identity management, and regulatory compliance into the foundational design of all enterprise systems. This responsibility includes defining zero-trust perimeters, establishing identity federation standards, conducting architectural risk assessments, and ensuring compliance with industry regulations.

Agentic skills generate gap analysis reports, scan firewall rules, verify identity provider configurations, check encryption standards, and draft automated policy scripts. Human skills define the acceptable risk thresholds, interpret complex regulatory mandates, design transitional security perimeters, and approve the final security posture.

For example, an architect first establishes the zero-trust security mandates for a newly acquired subsidiary’s network integration. An AI agent then scans the subsidiary’s existing firewall rules, identity providers, and access logs, generating a comprehensive gap analysis report against the enterprise zero-trust baseline. The architect reviews the report, prioritizes the critical identity federation gaps, and designs the transitional security perimeters. The agent subsequently generates the automated policy scripts to enforce the new conditional access rules across the integrated environment. Finally, the architect conducts a final risk assessment and approves the secure onboarding of the subsidiary’s assets.

1.4.8. Stakeholder Alignment and Technical Roadmapping

Stakeholder alignment and technical roadmapping navigates the complex landscape of executive expectations, budget constraints, and technical realities to secure consensus on the IT transformation journey. This responsibility includes negotiating scope, mapping critical path dependencies, communicating technical trade-offs, and managing the enterprise technology roadmap.

Agentic skills summarize resource allocations, map critical path dependencies, draft revised technical roadmaps, update portfolio management tools, and generate executive communication decks. Human skills evaluate the strategic validity of timeline adjustments, negotiate scope with executive sponsors, protect the architectural integrity of the roadmap, and communicate complex technical trade-offs to non-technical leaders.

For example, business leaders first request an accelerated timeline for a major platform modernization program. An AI agent then analyzes the current resource allocation, maps the critical path dependencies, and drafts a revised technical roadmap highlighting the resulting infrastructure bottlenecks. The architect reviews the roadmap, negotiates the scope adjustments with the executive sponsors, and determines which architectural milestones can be safely parallelized. Based on the architect’s negotiated baseline, the agent subsequently updates the portfolio management tools and generates the revised milestone communication decks. Finally, the architect presents the finalized technical roadmap to the board, securing alignment on the adjusted delivery expectations.

1.5. Changing Human and Agentic Involvement Across Enterprise Architecture Responsibilities

Human skills and agentic skills are involved throughout enterprise architecture, but their relative involvement differs across responsibilities and changes during task execution.

These involvement levels are not permanent allocations. A single responsibility contains actions where human and agentic skills compete as alternative capabilities and other actions where they collaborate sequentially, iteratively, or recursively.

The practical objective is to construct skill configurations that use the most suitable available capabilities for each part of the work and revise those configurations when task performance evaluation shows that another arrangement performs more effectively or reliably.

1.6. Working as an Enterprise Architect with Agentic AI

Agentic AI is becoming an increasingly powerful component of enterprise architecture. Enterprise architects should therefore adopt and integrate AI agents as complementary participants in professional workflows rather than treating them only as occasional tools for diagram generation or codebase scanning.

This shift does not reduce the importance of architectural expertise. Human professionals remain responsible for the occupation and its corresponding responsibilities. They understand business strategy, craft target-state visions, establish architectural principles, interpret regulatory mandates, evaluate structural trade-offs, mediate stakeholder expectations, control technology investments, evaluate task performance, and decide how human and agentic skills are configured and improved.

At the same time, agentic skills greatly expand the volume and variety of architectural analysis that can be processed. AI agents summarize extensive technology trends, scan millions of lines of code for dependencies, calculate complex cloud infrastructure costs, map enterprise-wide integration bottlenecks, check designs against compliance baselines, and generate infrastructure-as-code templates at a scale difficult to achieve through direct human effort alone.

Strong enterprise architecture practice is therefore not based on maximizing human work or agentic work independently. It is based on finding appropriate configurations of human skills and agentic skills for the responsibilities, tasks, subtasks, and actions being performed.

Where a human skill and an agentic skill can independently perform the same action, skills competition identifies which is better suited to the current context. Where work requires complementary capabilities, skills collaboration results in human and agentic skills being activated sequentially, iteratively, or recursively to achieve stronger task performance than either skill type achieves alone.

Task performance evaluation then determines whether selected skills and configurations satisfy predefined expectations. Successful skills are retained and reused, while inadequate skills are refined, reconfigured, or newly developed. The resulting capabilities return to an increasingly useful skill space that supports enterprise architecture and, where capabilities are reusable, other occupations as well.

For an enterprise architect, working effectively with agentic AI means treating AI as a powerful complementary capability integrated into everyday professional routines while maintaining human control of occupational responsibilities. Through appropriate skill selection, competition, collaboration, evaluation, and continuous improvement, human professionals and agentic AI work together to achieve enterprise architecture that is more efficient, scalable, adaptable, secure, and strategically aligned.