Investment Thesis

Agentia is building the control layer for enterprise AI operations.

Most AI vendors optimize model output. Enterprises still need a system that defines roles, constraints, approval logic and operating contracts before autonomous work starts. That is the gap Agentia is addressing.

Structured AI design

Governed execution

Partner-agnostic deployment

Why this matters

01

AI adoption is outpacing governance. Pilots are moving into operations before organizations have a durable design model for control, audit and accountability.

02

Execution vendors do not solve organizational design. Models, copilots and automation stacks can execute work, but they do not define the enterprise operating logic around that work.

03

The strategic layer is still open. There is room for a platform that sits above model providers and turns fragmented AI activity into a governed operating system.

866

structured catalog entries already present in the agent library inside the repo.

31

company-level control and governance fields in the current control-plane structure.

33 + 75

macro and mini setup fields supporting operating-model design and execution logic.

Agentia

Problem

02

Enterprise AI is moving from experimentation to sprawl.

The operating risk is no longer whether AI works in a demo. The risk is that multiple teams deploy disconnected agents, tools and prompts without a shared model for ownership, escalation, data boundaries or auditability.

Tool sprawl

Different teams adopt different agents, copilots and automations without a common design language.

Compliance exposure

Sensitive workflows start touching data, approvals and decisions before the company defines control boundaries.

Weak accountability

When outputs fail, enterprises often cannot explain who defined the role, the rules, the inputs or the allowed actions.

What breaks first

Design quality becomes the bottleneck.

Teams can now generate or automate work quickly, but they still struggle to define how AI should behave inside a real operating context.

Governance arrives late.

Policies are often layered on top after pilots spread, rather than embedded into the design of roles, flows and permissions.

Enterprise adoption slows down.

Executives become more cautious once AI moves from isolated experiments to cross-functional processes with real consequences.

Agentia

Market Gap

03

Capital crowded the execution layer. The control layer is still open.

Where most capital has gone

Foundation models and model APIs

Horizontal copilots and chat interfaces

Workflow automation and app connectors

Point agents for isolated business tasks

What enterprises still need

A design system for AI roles, constraints and decision rights

A structure that connects business goals, workflows and autonomy levels

An auditable contract layer between humans, agents and partner tools

A governance model that works across vendors instead of inside one stack

The wedge

Agentia can own the design and governance layer.

It does not need to win the model race. It needs to become the layer enterprises trust to define, authorize and monitor AI work.

Agentia

Platform Structure

04

Agentia structures AI work before execution begins.

866

catalog entries

Capability library

A large base of reusable agent definitions supports repeatable composition instead of blank-page design every time.

33

macro setup fields

Operating-model design

Macro agents define goals, responsibilities, KPIs, approvals and escalation logic at the business-role layer.

75

mini setup fields

Execution-level control

Mini agents define flows, I/O, tools, datasets, permissions and operational constraints in detail.

Control-plane logic

31

Company-level governance fields The current control-plane schema already captures structured organization, compliance and risk inputs.

A

Business goals connected to operating roles Agentia connects objectives, functions, data, approvals and outcomes in one design framework.

B

Execution can remain partner-agnostic Agentia does not need to replace downstream vendors if it becomes the layer that governs how work is defined and routed.

Agentia

Design Logic

05

Deterministic by design, not prompt-led by default.

Core principles already reflected in the repo

01

Structured inputs only The design philosophy rejects free-form setup when reliable operating behavior matters.

02

Reproducible outputs Same context should drive the same configuration logic, which matters for enterprise trust.

03

Auditable contracts Allowed inputs, outputs, actions and escalation rules can be made explicit rather than implied.

04

Constraint-based composition Compatibility, dependencies, risk and role logic can be validated systematically across teams.

System view

Layer 1

Company context

Structured profile, constraints, jurisdictions, risk tolerance and business goals.

Layer 2

Rule engine and constraint solver

Deterministic matching decides which agent structures fit and which combinations violate policy or risk logic.

Layer 3

Operational contract layer

Binding definitions for autonomy, approvals, inputs, outputs and audit requirements.

Agentia

Buyer Fit

06

The best initial buyers are regulated, cross-functional enterprises.

Agentia does not need to sell into every company using AI. The strongest early buyers are enterprises where AI rolls across departments, approval chains and regulated processes, making design discipline a budgeted need rather than a theoretical benefit.

Ideal conditions

Multiple departments are already testing AI tools independently.

The company operates with approval paths, audit requirements or sensitive data.

Leadership wants AI operating leverage without losing control of decisions.

Natural sectors

Regulated services and financial operations

Healthcare, insurance and compliance-heavy workflows

Complex B2B operators with cross-functional coordination

Commercial implication

This is a governance wedge, not a commodity automation wedge.

The buyer is paying to reduce design ambiguity, operational risk and rework as AI usage scales.

The message is strongest with enterprise operators and transformation leads.

That includes innovation teams, COO functions, digital transformation, compliance and IT governance stakeholders.

A narrow initial ICP is a strength.

It makes the story more fundable than a generic “AI for everyone” proposition.

Agentia

Why Now

07

The timing is driven by governance pressure, not by model hype alone.

01

Pilots are becoming operating workflows. As AI moves closer to production, companies need design standards and control rules they can defend internally.

02

Vendor fragmentation is increasing. Enterprises are unlikely to standardize on one model or one agent vendor, which makes an orchestration and governance layer more strategic.

03

Boards want ROI with accountability. Leadership is asking for AI leverage without accepting black-box deployment inside critical processes.

04

Structured control is becoming a buying criterion. Auditability, approvals, role clarity and operating boundaries are increasingly part of the enterprise purchase decision.

Agentia

Go To Market

08

Agentia can land as design software and expand into control infrastructure.

Land

AI operating-model design

Start with blueprinting, governance setup and agent-role definition for one business domain or one cross-functional workflow.

Expand

Control plane across teams

Extend from one use case into shared standards for permissions, escalation, audit logic and reusable components.

Scale

Recurring platform layer

Become the system that continuously governs how AI roles are defined, approved, versioned and monitored.

Channels

Direct plus ecosystem leverage

Sell with enterprise teams directly, while also benefiting from AI consultancies, integrators and execution-platform partners.

Analyst view: this is materially more credible than a pure services story if Agentia standardizes templates, contracts, schemas and governance workflows into productized recurring software.

Agentia

Defensibility

09

The moat compounds through structured enterprise design, not through one model API.

Moat 1

A structured catalog becomes harder to replicate as it deepens.

The value is not only in having entries, but in how roles, functions, outcomes, flows and constraints are normalized into reusable enterprise logic.

Moat 2

Operational contracts create switching friction.

Once teams rely on explicit approval paths, autonomy rules and audit definitions, the control layer becomes embedded in operations.

Moat 3

Governance data improves with deployment breadth.

Each enterprise implementation can enrich templates, control patterns and role definitions in ways that strengthen the product over time.

Moat 4

Vendor neutrality is strategically valuable.

If Agentia sits above model providers and execution stacks, it can remain relevant even as the underlying vendor landscape shifts.

Agentia

Ecosystem Positioning

10

Agentia can win above execution vendors, not against them.

Operating flow

Business goals What the company wants AI to improve, protect or automate.

Agentia design layer Roles, constraints, contracts, approvals, KPIs and structured flows.

Execution partners Model providers, workflow engines, copilots and enterprise systems.

Governance intent Policies, risk tolerance and operating boundaries.

Control plane Permissioning, escalation, audit and environment-wide standards.

Delivered outcomes Actions, recommendations, reports and monitored execution traces.

Investor implication

This positioning is strategically cleaner.

Agentia does not need to outbuild the model vendors. It needs to become the governance and design layer enterprises trust across those vendors.

It can ride ecosystem growth.

As more execution tools enter the market, the need for a unifying structure and control plane increases rather than decreases.

It supports partnership logic.

Execution vendors, consultancies and integrators can all benefit from a system that makes enterprise AI deployment more governable.

Agentia

Capital Logic

11

Funding should accelerate product depth, trust and distribution.

Product depth

Turn the current structured schemas, control-plane logic and catalog assets into a more complete enterprise product with packaged templates and deployment workflows.

Enterprise trust

Invest in security, compliance, administration, traceability and versioning features that make the platform easier to buy into regulated environments.

Channel leverage

Develop partnerships with integrators, AI consultancies and platform vendors that already have enterprise distribution.

Repeatable commercial motion

Move from custom narrative selling toward a sharper ICP, clearer use cases and a more standardized land-and-expand motion.

Senior-analyst framing: the investment case is stronger if Agentia is presented as enterprise AI operating infrastructure with product compounding, not as a generic agent studio or a design-heavy consulting layer.

Agentia

Closing View

12

Agentia targets the missing layer between AI ambition and enterprise execution.

The strongest fundraise story is not “another AI product.” It is a platform thesis: enterprise AI needs a system for role design, constraints, governance and operating contracts, and Agentia is building that layer with a structured foundation already visible in the product assets.

control plane

structured agent catalog

enterprise AI operating model

Contact

www.agentia.work marketing@agentia.work LinkedIn / company/agentia.work

Deck rebuilt for fundraising conversations: tighter narrative, more analytical positioning, and cleaner slide architecture with no decorative dead space.

Agentia

Our vision