The New Workforce Model (Cyrenza)

The limitations of the traditional workforce model make clear that improving professional capacity requires more than incremental process refinement or additional tools. As organisational complexity increases, the challenge shifts from storing information efficiently to producing high-quality knowledge work consistently, at scale, and under clear accountability. Addressing this challenge requires a fundamental change in how work is structured and how intelligence is distributed across people and systems.

This section introduces the Cyrenza workforce model as a response to that challenge. The model redefines the role of software within professional environments by moving beyond storage and presentation toward active participation in knowledge work. Rather than positioning technology as a passive support layer, Cyrenza embeds role-based AI Knowledge Workers directly into workflows, where they contribute structured execution across recurring cognitive tasks.

The purpose of this section is to establish the principles that govern this new workforce model. It explains how participation replaces passive tooling, how human judgment remains central, how cognitive labour is deliberately divided, and how these changes alter the mechanics of scaling professional capacity. The focus is not on automation in isolation, but on organisational design and the creation of a digital workforce that operates alongside human professionals within clear boundaries.

By the end of this section, you will understand how Cyrenza reframes professional work production, why this represents a workforce shift rather than a software upgrade, and how this model enables organisations to increase consistency, continuity, and analytical depth without relying solely on headcount growth.

2.1 From Storage to Participation

2.1.1 Concept and Purpose

From storage to participation describes a shift in how organisations design professional work. Traditional enterprise software has been built to store information, format outputs, and distribute materials across teams. Professional reasoning, interpretation, and synthesis have remained primarily human-led. The Cyrenza workforce model introduces a different structure. Software becomes a participant in the production of knowledge work through role-based AI Knowledge Workers that contribute directly to execution under human direction.

The purpose of this shift is to increase the reliability and throughput of professional work without relying on proportional increases in human time spent on repetitive cognitive tasks.

2.1.2 What Participation Means in a Professional Context

Participation, in this model, refers to active contribution to work execution. This contribution is not limited to generating text. It includes producing structured reasoning outputs that support decision processes and operational delivery. Participation involves the ability to take a defined task objective, apply a consistent working method, and produce outputs that are aligned to professional expectations.

Within Cyrenza, participation is organised through role-based workers that mirror functional responsibilities found in human teams. Each worker operates within defined boundaries and is aligned to a category of work such as analysis, synthesis, evaluation, or structured reporting.

2.1.3 The Role of AI Knowledge Workers

AI Knowledge Workers in Cyrenza function as digital colleagues with defined roles. Their value comes from consistent execution within a clearly scoped responsibility. This enables work to be produced with greater uniformity across teams and projects, even when tasks recur frequently or require sustained iteration.

AI Knowledge Workers contribute to several categories of cognitive labour that are common across professional functions:

Analysis

Knowledge Workers assist by organising inputs, exploring scenarios, comparing options, and producing structured analytical outputs. This strengthens decision preparation by increasing the speed and depth at which information can be processed.

Pattern detection

Knowledge Workers identify signals, trends, and anomalies across datasets, documents, or operational histories. Pattern detection supports early identification of risks, opportunities, and areas requiring investigation.

Structuring of information

Knowledge Workers transform unstructured material into structured formats suitable for professional use. Examples include converting notes into briefs, consolidating multi-source inputs into a coherent summary, or producing consistent reporting formats.

Iterative refinement

Knowledge Workers support repeated improvement cycles by incorporating feedback, updating assumptions, and refining outputs while maintaining the integrity of the task objective and constraints.

These contributions reduce the manual assembly work that often consumes a large portion of professional time.

2.1.4 The Knowledge Operations Layer

The combined effect of these contributions is the formation of a knowledge operations layer that runs alongside the human team. This layer increases organisational capacity in a way that is operationally meaningful. It supports continuity of work across time, standardises output formats, and accelerates recurring work cycles.

A knowledge operations layer changes how work progresses. Instead of relying on individuals to repeatedly perform similar cognitive steps from scratch, the system provides structured execution support that can be applied consistently across workflows. This supports faster progress on complex workstreams and reduces variance caused by workload fluctuations or differences in individual working styles.

2.1.5 Consistency Across Projects and Teams

Organisations frequently require consistent outputs across multiple teams, regions, or business units. Consistency affects governance, stakeholder trust, and decision quality. Cyrenza is designed to support consistent work production by standardising how certain categories of work are executed and how outputs are structured.

This consistency is particularly valuable for tasks that occur repeatedly across the organisation, such as:

• Drafting structured outputs and briefs

• Consolidating information from multiple documents and sources

• Producing comparisons of alternatives and trade-offs

• Preparing decision-ready summaries for stakeholders

• Maintaining a consistent narrative between analysis and reporting

When these tasks are performed repeatedly by different individuals, variation is common. A role-based AI workforce reduces this variation by executing recurring cognitive steps with consistent structure and method, while still requiring human oversight and judgment.

2.1.6 Impact on Professional Capacity and Work Rhythm

Participation changes the allocation of human effort. Human professionals spend less time on manual assembly and repetitive processing and more time on interpretation, prioritisation, stakeholder alignment, and judgment. This shift improves the work rhythm of teams by increasing momentum. Work moves forward with fewer stalls caused by bottlenecks, delays in drafting, or repeated reconstruction of background context.

The effect is not simply faster outputs. The effect is a more stable production system that supports analytical depth even under operational pressure. When execution support is consistent, teams can allocate more attention to the higher-value aspects of professional work, including decision framing, governance, and long-horizon planning.

2.2 The Primacy of Human Judgment

2.2.1 Concept and Principle

The Cyrenza workforce model is built on a governance-first principle: human judgment remains the ultimate authority in professional work. AI Knowledge Workers contribute to execution by producing analysis, structure, and draft artefacts, but decisions and accountability remain with the human professional. This principle is essential in environments where decisions carry financial, legal, operational, or reputational consequences.

The distinction between execution support and decision authority is not a procedural detail. It is a structural requirement for responsible adoption of an AI-augmented workforce, particularly in sectors where traceability, defensibility, and compliance expectations are high.

2.2.2 Judgment as a Professional Responsibility

Professional judgment involves more than selecting an option from a list. It includes:

• Defining what the organisation is trying to achieve

• Interpreting trade-offs across competing objectives

• Assessing risk and uncertainty

• Considering stakeholder impact and timing

• Applying policy, ethics, and governance constraints

• Accepting accountability for outcomes

These responsibilities cannot be reduced to output generation. They depend on organisational mandate, contextual awareness, and responsibility for consequences. Cyrenza supports judgment by improving the quality and clarity of inputs, while preserving human authority over the decision itself.

2.2.3 Clear Separation of Roles in the Cyrenza Model

Cyrenza formalises a clear separation between the contributions of AI Knowledge Workers and human professionals.

AI Knowledge Workers contribute execution strength

They support work production through structured cognitive tasks such as:

• Analysing data and documents under defined objectives

• Identifying patterns, risks, and inconsistencies

• Structuring information into usable professional formats

• Generating options and supporting comparisons

• Refining outputs through iterative improvement cycles

Their contribution is measured by reliability, clarity, and consistency of work products.

Human professionals retain decision authority

They remain responsible for:

• Defining objectives and success criteria

• Setting constraints, boundaries, and acceptable risk

• Selecting what information is materially relevant

• Validating outputs and challenging assumptions

• Deciding on actions, recommendations, or approvals

• Owning outcomes and explaining decisions to stakeholders

This separation supports governance and prevents responsibility from drifting away from accountable roles.

2.2.4 Direction, Constraints, and Validation as the Human Control Layer

In the Cyrenza model, human professionals exercise control through three core activities that shape the quality and appropriateness of outcomes.

Objective definition

Professionals clarify the purpose of the work and the decision context. This includes the operational conditions under which the output will be used and the standards it must meet.

Constraint setting

Professionals define boundaries such as time horizon, risk tolerance, format requirements, and prioritisation rules. Constraints prevent scope drift and ensure that outputs remain aligned to organisational needs.

Validation and review

Professionals assess outputs for correctness, completeness, and relevance. Validation includes checking assumptions, evaluating reasoning, and determining whether the output is suitable for decision-making.

These activities form the human control layer that ensures the organisation benefits from execution support without compromising decision responsibility.

2.2.5 Reallocation of Professional Effort

The primacy of human judgment changes how professionals allocate time and attention. In traditional workflows, skilled professionals often spend large portions of their time on manual assembly tasks that are necessary but not strategically differentiating. These tasks include consolidating documents, formatting reports, producing repeated analyses, and preparing standard deliverables.

With Cyrenza, a portion of this manual assembly work is reduced through execution support. Professional effort shifts toward higher-value activities such as:

• Interpreting results and identifying implications

• Prioritising actions and sequencing work

• Aligning stakeholders and managing decision processes

• Applying domain expertise to risk and trade-offs

• Making final decisions and ensuring accountability

This reallocation is not a reduction in professional responsibility. It is an improvement in where professional judgment is applied.

2.2.6 Traceability and Defensibility of Work Outputs

A core requirement for human judgment in professional environments is the ability to justify decisions. Cyrenza supports this requirement by producing outputs that are structured and aligned to the task context, with reasoning presented in a form that can be reviewed.

Traceability in this context refers to the ability to connect:

• The objective and constraints of the task

• The evidence or information considered

• The reasoning steps that led to a conclusion

• The uncertainties and assumptions that remain

• The final decision taken by the accountable professional

When work is traceable, human judgment becomes easier to exercise responsibly. Review becomes more efficient, and governance standards become easier to uphold.

2.2.7 Implications for Accountability and Governance

The Cyrenza model strengthens accountability by clarifying where responsibility resides. Outputs produced by AI Knowledge Workers remain inputs to human-led decision-making. The accountable human role retains ownership of outcomes, including the obligation to review and validate work before it influences decisions.

This structure supports organisational governance by ensuring that professional standards are maintained even as capacity increases. It also supports regulated and high-stakes environments by reinforcing defensible decision processes.

2.3 Division of Cognitive Labour

2.3.1 Concept and Definition

Division of cognitive labour refers to the intentional allocation of different types of thinking work to different contributors within a professional system. In the Cyrenza workforce model, this allocation is designed rather than incidental. Human professionals and AI Knowledge Workers are assigned complementary responsibilities based on the nature of the cognitive task, the level of judgment required, and the accountability attached to outcomes.

This division is a form of organisational design. It clarifies who does what, under which conditions, and with what standards of review. It supports higher performance by reducing wasted effort and improving consistency across recurring knowledge tasks.

2.3.2 Why Cognitive Labour Must Be Divided

Professional work contains multiple layers of cognition that often become blended in traditional workflows. A single deliverable can require problem definition, data processing, scenario exploration, synthesis into narrative form, and stakeholder-ready communication. When one person must perform all layers, the quality of execution is constrained by time, attention, and workload.

A deliberate division of cognitive labour addresses three organisational challenges:

• Complexity management, by separating tasks that require deep judgment from tasks that require structured execution

• Capacity scaling, by increasing throughput for recurring work without proportional headcount growth

• Consistency improvement, by standardising how repeatable cognitive tasks are executed and reviewed

The goal is not to reduce professional responsibility. The goal is to ensure that professional responsibility is exercised where it matters most.

2.3.3 Categories of Cognitive Work in the Cyrenza Model

The Cyrenza workforce model distinguishes between categories of cognitive work that have different characteristics and different risk profiles. These categories can be grouped into human-led work and AI-supported work, with clear boundaries between them.

Human-Led Cognitive Work

Human professionals retain authority over tasks that require judgment, accountability, and organisational mandate. These tasks include:

Goal definition and problem framing
Professionals define what the organisation is trying to achieve, what the decision requires, and what constraints apply. This includes deciding what success means, what trade-offs matter, and what the acceptable risk range is.

Interpretation of trade-offs and prioritisation
Professionals evaluate competing objectives, balance stakeholder interests, and make prioritisation choices. This includes timing, sequencing, and resource allocation decisions that require situational awareness.

Judgment under uncertainty
Professionals evaluate incomplete information and decide how much confidence is sufficient for action. They assess risk, determine escalation thresholds, and decide what must be validated before commitment.

Accountability and decision ownership
Professionals remain responsible for outcomes and for explaining the reasoning behind decisions to stakeholders. Accountability includes governance, compliance, and the practical consequences of decisions.

These tasks define the leadership function within knowledge work and remain human-owned in Cyrenza.

AI-Executed Structured Cognitive Work

AI Knowledge Workers contribute execution strength through structured cognitive tasks that benefit from repeatability, speed, and consistency. These tasks operate within defined boundaries and are subject to human review. They include:

Analysis and structured exploration
Agents process data and documents, run scenario comparisons, identify drivers, and produce structured analytical outputs. This supports decision preparation by expanding the range and depth of exploration available to the human professional.

Synthesis and consolidation
Agents consolidate information across sources, identify common themes, and structure findings into coherent summaries. This is especially valuable when information is dispersed across documents, meetings, and historical records.

Formatting and professional structuring
Agents produce outputs in structured formats that match professional expectations such as briefs, memos, reports, and decision packs. Consistent structure improves usability and accelerates review.

Option generation and comparison
Agents generate alternative approaches, outline trade-offs, and present options in comparable forms. This supports better decision-making by widening the option set and clarifying implications.

Iterative refinement
Agents incorporate feedback, adjust outputs to new constraints, and refine deliverables while maintaining continuity. Iteration becomes faster and more consistent when the system can improve work products without repeated manual rewriting.

These tasks reduce repetitive cognitive load and accelerate production without transferring decision authority away from humans.

2.3.4 Matching Work to the Most Suitable Contributor

The value of the division of cognitive labour comes from matching tasks to the contributor best suited to execute them. Several matching principles apply:

Repeatability
Tasks that recur frequently and benefit from consistent structure are well suited to AI Knowledge Workers, subject to human review.

Complexity and scale
Tasks that involve large volumes of information, multi-document consolidation, or broad scenario exploration benefit from AI execution because they require sustained processing effort.

Judgment and consequence
Tasks that determine direction, risk posture, and final decisions remain with human professionals because accountability resides in human roles.

Governance requirements
Work that must be defensible to stakeholders requires clear human review and approval, supported by structured AI outputs that improve traceability.

This matching creates a stable operating system for knowledge work, rather than relying on informal delegation patterns.

2.3.5 Oversight as a Core Component of the Model

Division of cognitive labour does not remove the need for professional oversight. It formalises it. Human professionals validate outputs, challenge assumptions, and determine readiness for decision-making. This oversight includes:

• Checking that the output aligns with the objective and constraints

• Verifying that reasoning is coherent and supported by evidence

• Identifying gaps, uncertainties, and missing considerations

• Deciding whether additional work or escalation is required

Oversight ensures that execution strength translates into decision quality.

2.3.6 Consistency and Institutional Capability

A key benefit of this division is consistency. When structured cognitive tasks are executed in a repeatable manner, organisations reduce variability across teams and projects. This creates institutional capability that is not dependent on a small number of individuals.

Consistency also strengthens governance. Outputs become easier to review because they follow standard structures. Knowledge becomes easier to reuse because it is captured in organised forms. Teams spend less time reconstructing context and more time advancing work.

2.4 Operational Comparison of Workforce Models

2.4.1 Purpose of the Comparison

This subsection provides an operational comparison between the traditional workforce model and the Cyrenza workforce model. The purpose is to clarify how work is produced, how reasoning is organised, and how capacity scales under each approach. The comparison is framed around observable dimensions that matter to organisations, including information flow, cognitive load, output behaviour, system structure, and scaling logic.

The comparison supports curriculum clarity by making the workforce shift concrete. Learners should be able to recognise how daily work changes in practice, not only in principle.

2.4.2 Comparison Table

2.4.3 Information Flow

Storage-Oriented Information Flow

In the traditional model, software acts primarily as a repository. Data and documents are stored across applications, and professionals retrieve them when needed. The flow of information depends on human effort to locate the right sources, decide what is relevant, interpret meaning, and integrate findings into a coherent output. Information can be abundant while understanding remains scarce, because understanding must be constructed manually each time work begins.

This creates common organisational challenges such as duplicated effort, inconsistent interpretations, and slow decision cycles. Information exists, yet it does not automatically become usable knowledge.

Processing-Oriented Information Flow

In the Cyrenza model, information flow is shaped by AI Knowledge Workers that process information in support of a defined objective. Processing means structuring, synthesising, comparing, and preparing information for decision use. The organisation gains a mechanism that converts stored material into structured outputs with greater speed and repeatability.

Processing-oriented information flow supports continuity across workflows because the system can repeatedly apply consistent methods to similar tasks. This reduces the need for professionals to reconstruct understanding from scratch each time work is initiated.

2.4.4 Cognitive Load

Full-Cycle Human Cognition

Traditional workflows require professionals to carry both the high-value judgment tasks and the lower-level assembly tasks. The same person often performs data preparation, analysis, synthesis, formatting, and communication packaging. This concentration of cognitive load limits throughput and increases the risk of error under time pressure. It also creates a direct coupling between workload and output quality.

Directed and Validated Cognition

In the Cyrenza model, cognitive load is deliberately redistributed. Human professionals retain tasks that require judgment and accountability, such as defining goals, setting constraints, evaluating trade-offs, and approving decisions. AI Knowledge Workers execute structured cognitive tasks that support these decisions, such as analysis, consolidation, structuring, and iterative refinement.

This redistribution changes the work profile of professionals. More time is spent on direction, validation, stakeholder alignment, and decision-making. Less time is consumed by repetitive assembly and formatting work.

2.4.5 Work Outputs

Static Outputs and Snapshot Artefacts

Under the old model, outputs are typically produced as snapshots. A report, spreadsheet, or presentation captures the state of understanding at a moment in time. As conditions change, these artefacts require manual revision to remain accurate. Over time, organisations accumulate large volumes of partially outdated work products, creating uncertainty about what remains current and reliable.

This snapshot behaviour weakens reuse and increases rework. Teams often redo analysis because older artefacts are difficult to interpret or validate without rebuilding context.

Living Analysis and Extendable Work Products

Under the Cyrenza model, work outputs are designed to be refined and extended as objectives, data, and constraints change. The output is treated as an evolving work product rather than a finalised snapshot. Iteration becomes easier because structured execution support can update, adjust, and rebuild work quickly while maintaining continuity of reasoning.

This improves operational resilience. Work can remain aligned to current conditions without requiring repeated rebuilding from the beginning.

2.4.6 Tool Structure

General-Purpose Tool Ecosystems

Traditional enterprises rely on general-purpose tools designed for broad use cases. These tools support many activities, but they do not encode role-specific reasoning methods. The organisation therefore depends on individuals to bring consistent professional structure to each task. Where structure exists, it comes from training, templates, and oversight rather than from the tool itself.

This is a major source of variability across outputs, since different professionals bring different methods and levels of rigour.

Role-Specific AI Knowledge Workers

Cyrenza introduces role-based AI Knowledge Workers as a structural layer that mirrors functional specialisation found in human organisations. Role-specific workers allow recurring tasks to be executed with consistent reasoning patterns, output formats, and boundaries. This supports repeatability across teams and projects, especially for work that is frequently performed across functions such as analysis, reporting, review, and synthesis.

Role-specific structure reduces method drift and improves the ability to institutionalise best practices across the organisation.

2.4.7 Scaling Logic

Scaling Through Hiring

When demand rises under the traditional model, capacity increases mainly through additional headcount or extended hours. This increases cost and increases coordination load. Management oversight, review cycles, and alignment activities become larger components of the work. Over time, productivity gains flatten due to the rising cost of coordination and integration.

Scaling Through Delegation and Operational Design

In the Cyrenza model, scaling is supported through deliberate delegation to role-based AI Knowledge Workers and through operational design of workflows. Capacity increases when structured cognitive tasks are executed reliably by the digital workforce, while human professionals focus on judgment and oversight. This reduces the need to expand headcount at the same rate as workload growth and reduces bottlenecks caused by overloaded experts.

Scaling becomes an organisational design activity focused on workflow structure, role definition, governance, and validation standards.

2.4.8 Educational Implication for Workforce Transformation

This comparison demonstrates a shift in how professional work is produced and governed. The change is operational rather than cosmetic. It affects daily execution, review practices, continuity of knowledge, and the mechanics of scaling. Learners should understand that the model introduces a workforce structure that increases capacity through participation in work production, while preserving human authority over decisions and accountability.

2.5 Digital Workforce as a Capacity Multiplier

2.5.1 Concept and Definition

A capacity multiplier is a mechanism that increases an organisation’s ability to produce high-quality work without requiring proportional increases in human labour. In the Cyrenza workforce model, the capacity multiplier is a role-based digital workforce of AI Knowledge Workers that contributes directly to professional work execution under human oversight. This expands throughput, improves consistency, and supports continuity of analysis across longer time horizons.

The objective is not simply faster output. The objective is a more reliable and scalable production system for knowledge work, suitable for environments where accountability and defensibility are required.

2.5.2 Why Capacity Has Become a Strategic Constraint

Many organisations face a persistent gap between the volume of work demanded and the cognitive capacity available to deliver it. This gap emerges from several pressures that are common across sectors:

• Increased complexity of decisions and operating environments

• Greater volume of information requiring interpretation and synthesis

• Rising expectations for speed and responsiveness

• Constraints on hiring, training, and retention of specialised talent

• Higher standards for governance, traceability, and stakeholder communication

Traditional scaling approaches rely on hiring and extended hours. These approaches increase cost and coordination demands. They also tend to increase variability as more contributors and more handoffs enter the workflow.

A capacity multiplier addresses the gap by strengthening execution in a way that remains stable as volume grows.

2.5.3 How the Digital Workforce Multiplies Capacity

Cyrenza multiplies capacity by shifting part of the execution burden away from human professionals while preserving decision authority and accountability. The multiplier effect arises through three mechanisms.

Reduction of Repetitive Cognitive Load

A significant proportion of knowledge work involves recurring cognitive tasks such as consolidation, formatting, summarisation, option generation, and repeated analysis updates. These tasks are necessary, but they consume time that could otherwise be spent on higher-value judgment tasks. AI Knowledge Workers execute these recurring tasks consistently, reducing the manual effort required to produce decision-ready work products.

Increased Reliability of Work Outputs

Reliability improves when recurring work is produced in structured formats, using consistent reasoning patterns, and aligned to defined role responsibilities. AI Knowledge Workers support reliability by producing outputs that follow repeatable structures and by reducing variability that often results from differences in individual working styles, workload pressure, and time constraints. Human professionals retain responsibility for validation and approval, ensuring governance standards are maintained.

Sustained Analytical Depth Over Time

In traditional workflows, analytical depth often declines under operational pressure because time is scarce and cognitive load is high. A digital workforce enables teams to sustain deeper analysis across longer horizons by maintaining execution capacity even when volume increases. This supports ongoing scenario exploration, continuous refinement of work products, and improved readiness for decision cycles.

2.5.4 The Role of Human Professionals in a Multiplied System

Capacity multiplication does not reduce human responsibility. It repositions human effort toward functions where judgment is essential. Human professionals remain central to:

• Defining objectives and success criteria

• Setting constraints, standards, and acceptable risk levels

• Evaluating trade-offs and applying domain expertise

• Validating outputs and challenging assumptions

• Approving decisions and owning outcomes

• Explaining decisions to stakeholders and governance bodies

The digital workforce strengthens execution, while humans maintain authority and accountability. This structure supports professional standards and protects decision quality.

2.5.5 Practical Organisational Benefits

A digital workforce functioning as a capacity multiplier produces several measurable benefits in professional environments.

Higher Analytical Throughput

Teams can process more work within the same time horizon because structured execution is supported by AI Knowledge Workers. Throughput increases through faster consolidation, quicker drafting of structured outputs, and more efficient iteration.

Improved Consistency Across Teams

When recurring cognitive tasks follow consistent structures and reasoning patterns, output becomes more uniform. This reduces method drift, improves comparability across projects, and strengthens organisational standards.

Reduced Bottlenecks Around Key Individuals

High-skill professionals often become bottlenecks in traditional models because they are required to perform both high-value judgment tasks and lower-level assembly work. Execution support reduces pressure on these roles, enabling them to focus on oversight and decision-making where they add the most value.

Stronger Knowledge Continuity

When work products are maintained and refined over time, rather than repeatedly rebuilt, organisations preserve continuity. Teams spend less time reconstructing context and more time advancing work.

Governance Support and Defensibility

Structured outputs improve review and sign-off processes. When the reasoning chain and assumptions are presented clearly, decisions become easier to validate and explain. This is particularly important in environments where professional standards require traceability and accountability.

2.5.6 Scaling Without Proportional Headcount Growth

Under the Cyrenza model, organisations can expand decision support capability and analytical throughput without matching growth in headcount. Scaling is achieved through delegation to role-based AI Knowledge Workers and through workflow design that integrates execution support with human validation.

This changes how leaders think about capacity planning. Instead of treating staffing levels as the primary constraint, capacity becomes a function of workflow design, role definition, validation protocols, and the effective deployment of the digital workforce alongside human teams.