AI Engineer · Published 2026-08-28

AI-Native Organisations Run on Skills: How to Structure and Scale Them — Imad Touil, QuantumBlack

privateReview: unreviewedConfidence: high
agentic software stackskills governancecoding agent harnesstechnical debtcentralized platformprogressive disclosureMCP servers
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Summary

Overview

  • Speaker: Imad Touil
  • Channel: AI Engineer
  • Main topic: How AI-native organizations structure, govern, and scale skills within agentic software stacks and enterprise product delivery pipelines.
  • Purpose: To provide enterprise leaders and AI engineers with a strategic and architectural framework for organizing, governing, and scaling skills within agentic workflows to ensure high productivity, quality, and security. Imad Touil, Distinguished Engineer at QuantumBlack by McKinsey, breaks down the agentic software stack and explains why AI-native organizations must run on governed skills. He discusses the evolution of product delivery pipelines at scale, the limitations of ungovnered coding agents, and why 'skills' represent the new executable, portable, and cheap unit of enterprise know-how. He presents a framework for scaling skills through individuals, teams, and centralized platforms with robust governance, and demonstrates simulation results of skill governance across enterprise teams.

Topic Map

The Agentic Software Stack and Its Two Loops

  • Explanation: Explaining the inner loop (coding agent harness) and outer loop (workflows, skills, sub-agents, MCP servers, hooks) of the agentic software stack, supported by enablement components like sandboxes and model gateways.
  • Key claims:
    • The agentic software stack has two loops: an inner coding agent loop and an outer workflow loop.
    • Workflows act as harness blueprints that shape the behavior of coding agents in runtime.
    • Skills, sub-agents, MCP servers, and hooks are core components of the outer loop.
  • Examples:
    • Context layers containing project instructions, MCP schemas, memory, and retrieved codebase contents.
  • Terminology:
    • coding agent harness
    • inner loop
    • outer loop
    • MCP servers
    • model gateway
  • Why it matters: Understanding the stack architecture is necessary to build deterministic, end-to-end product delivery pipelines in enterprise settings.

Enterprise Product Delivery at Scale vs. Standard Pipelines

  • Explanation: Contrasting simple four-step AI development pipelines (specify, design, tasks, implement) with complex enterprise product delivery lifecycles across strategy, discovery, product development, data product delivery, and platform engineering.
  • Key claims:
    • Standard four-step AI development pipelines do not reflect enterprise complexity at scale.
    • Enterprise product delivery involves multiple DLCs across strategy, market research, discovery, data pipelines, platform engineering, and launch.
    • Real enterprise landscapes are highly fragmented with diverse DLCS across departments and platforms.
  • Examples:
    • Strategy and insights feeding into product delivery, platform engineering, and continuous optimization.
  • Terminology:
    • product increment
    • platform engineering
    • product delivery lifecycle
  • Why it matters: Organizations must account for end-to-end enterprise complexity rather than isolated coding tasks when deploying AI agents.

The Anatomy of Skills and Design Principles

  • Explanation: Detailing the design principles of skills (reusable, discoverable, specialized, composable, consistent, cost-efficient) and how they make enterprise know-how executable, portable, and cheap.
  • Key claims:
    • Skills serve as blueprint harnesses that shape coding agent behavior at runtime.
    • Skills make organizational know-how executable, portable, and cheap.
    • Skills need to follow strict design principles similar to microservices architecture.
  • Examples:
    • Data retention policy skill example showing SKILL-ID, description, and rules in YAML format.
  • Terminology:
    • skills registry
    • design principles
    • prompt injection
    • progressive disclosure pattern
  • Why it matters: Without well-structured skills, agent workflows lack determinism and lead to high token usage and low quality.

Ungoverned Skills as Technical Debt and Governance Framework

  • Explanation: Highlighting the risks of ungoverned skills (duplication, poor quality, poor discoverability, lack of ownership, maintainability issues, composability gaps, security risks, permission issues) and proposing a three-tier scaling and governance model (individual, team, centralized platform).
  • Key claims:
    • Ungoverned skills create a new class of technical debt including duplication, low quality, and security risks.
    • Scaling skills requires a structured framework across individual, team, and centralized platform levels.
    • Centralized platforms provide catalogs, metadata, versioning, access control, and observability.
  • Examples:
    • Simulation dashboard showing 15 teams and skill governance impact on productivity, quality, and security across 6 months.
  • Terminology:
    • technical debt
    • skills governance
    • centralized platform
    • versioning
    • access control
  • Why it matters: Governance ensures that AI-native organizations avoid code duplication, security vulnerabilities, and quality degradation at scale.

Key Points

The agentic software stack has two loops

  • Explanation: The inner loop is the coding agent harness, and the outer loop comprises workflows, skills, sub-agents, MCP servers, and hooks.
  • Evidence: Architectural breakdown of the agentic software stack in the presentation slides.
  • Practical implication: Engineers must design both loops to manage agent behavior effectively in production.

Skills make organizational know-how executable, portable, and cheap

  • Explanation: Skills encapsulate best practices, conventions, and regulatory policies into structured, reusable units that agents can execute deterministically.
  • Evidence: Skill design principles and YAML examples comparing raw agent prompts to structured skills.
  • Practical implication: Teams can reduce token usage and improve output quality by adopting standardized skills.

Ungoverned skills create a new class of technical debt

  • Explanation: Without governance, organizations face duplication, low quality, discoverability issues, ownership gaps, security flaws, and permission conflicts.
  • Evidence: Analysis of technical debt categories associated with ungoverned skills.
  • Practical implication: Enterprises must establish a centralized skills registry and governance process before scaling agentic workflows.

Scaling skills requires a three-tier model

  • Explanation: Organizations should empower individuals to create/extract skills, enable teams to collaborate and improve them, and maintain a centralized platform for governance, cataloging, and versioning.
  • Evidence: Step-by-step scaling architecture diagram from individual level to team level to centralized platform and organization level.
  • Practical implication: Enables seamless sharing and reuse of high-quality skills across the entire enterprise.

Frameworks, Models & Processes

Skill Design Principles

  • How it works: Applying software design principles (similar to microservices) to the creation and maintenance of agent skills.
  • Components:
    • Reusable
    • Discoverable
    • Specialized
    • Composable
    • Consistent
    • Cost-efficient
  • When to use: Whenever defining and implementing skills for coding agents or enterprise workflows.

Three-Tier Skills Scaling Framework

  • How it works: Scaling skills across organizational layers to ensure quality, reuse, and governance.
  • Components:
    • Individual level (Create/extract, test/improve, use, publish)
    • Team level (Collaborative improvement and sharing)
    • Centralized platform level (Cataloging, metadata, discovery, access control, versioning, observability, governance)
  • When to use: When rolling out agentic workflows and coding agent harnesses across an enterprise.

Examples & Case Studies

Simulation of 15 enterprise teams over 6 months with and without skills governance.

  • Illustrates: How ungoverned skills lead to fragmented productivity, duplicated efforts, and variable quality, whereas governance aligns teams around shared high-quality skills.
  • Lesson: Centralized skills governance significantly improves organizational productivity, code quality, and security compliance.

Actionable Takeaways

  • Immediate:
    • Adopt skill design principles for all agentic workflows.
    • Establish a skills registry within your organization.
    • Leverage the progressive disclosure pattern to manage context windows efficiently.
  • Strategic:
    • Treat skills as the fundamental unit of enterprise know-how in AI-native organizations.
    • Implement a three-tier governance framework across individuals, teams, and centralized platforms.
    • Integrate skills governance into platform engineering and enterprise product delivery pipelines.
  • Questions to investigate:
    • How are skills currently created and shared across your engineering teams?
    • What governance mechanisms exist for checking the security and compliance of agent skills?
    • How can your organization transition from ad-hoc prompting to executable, portable skills?

Claims Worth Verifying

  • The agentic software stack has two distinct loops: inner coding agent harness and outer workflow loop. (architectural framework)
  • Skills reduce token usage and increase determinism in agent execution. (performance claim)
  • Ungoverned skills create a new class of technical debt encompassing duplication, quality, discoverability, ownership, maintainability, composability, security, and permissions. (conceptual framework)

Notable Quotes

"AI-native organisations runs on Skills" (at 0:23) "The agentic software stack has two loops" (at 1:47) "Ungoverned skills create a new class of technical debt." (at 18:41)

Compressed Summary

  • Agentic software stacks consist of an inner coding agent loop and an outer workflow loop.
  • Skills are executable, portable, and cheap units of enterprise know-how.
  • Design principles for skills include reusability, discoverability, specialization, composability, consistency, and cost-efficiency.
  • Ungoverned skills introduce technical debt such as duplication, security risks, and poor quality.
  • Scaling requires a three-tier framework spanning individuals, teams, and a centralized platform with governance.
  • Keywords: agentic software stack, skills governance, coding agent harness, technical debt, centralized platform
  • Core insight: AI-native enterprises achieve deterministic scale and high productivity by treating skills as governed, reusable software components rather than ad-hoc prompts.

Core insights

5
Architecturemedium noveltystrong evidence

The agentic software stack has two loops: an inner loop of the coding agent harness and an outer loop of workflows, skills, sub-agents, MCP servers, and hooks. Workflows act as 'harness blueprints' that shape coding agent behavior at runtime, so deterministic enterprise delivery depends on controlling the outer loop, not just improving the model.

Why it matters

It relocates agency: deterministic product outcomes are engineered through externally governed workflow components rather than left to the agent's discretion. Engineers should architect both loops and treat the outer loop as the primary control surface.

Generalization

Any agentic system intended for production needs a separation between the autonomous inner action loop and an outer loop of governed context, tools, and workflows that constrain and direct it.

The inner loop is the coding agent harness, and the outer loop comprises workflows, skills, sub-agents, MCP servers, and hooks.
Open source video
Workflows act as harness blueprints that shape the behavior of coding agents in runtime.
Open source video
Mental Modelhigh noveltystrong evidence

A 'skill' is a new executable unit of organizational know-how—a structured, reusable, versionable artifact that encodes best practices, conventions, and policy so agents execute them deterministically. This is distinct from a natural-language system prompt or ad-hoc instruction.

Why it matters

It shifts skills from documentation into software engineering artifacts: they deserve IDs, descriptions, rules, registries, versioning, and tests. This makes enterprise knowledge auditable, composable, and independently improvable.

Generalization

The core reusable abstraction in an agentic enterprise is not the prompt or the agent; it is the executable knowledge module (skill) that an agent can invoke in a constrained way.

Skills encapsulate best practices, conventions, and regulatory policies into structured, reusable units that agents can execute deterministically.
Open source video
Data retention policy skill example showing SKILL-ID, description, and rules in YAML format.
Open source video
Failure Modemedium noveltystrong evidence

Ungoverned skills create a distinct new class of technical debt—duplication, poor quality, poor discoverability, ownership gaps, composability gaps, security risks, and permission issues—analogous to code-level technical debt but at the level of agent behavior.

Why it matters

Organizations will scale agent usage rapidly, and without governance they multiply inconsistent and unsafe behaviors across teams. Agent technical debt must be managed with registries, ownership, and versioning just like code.

Generalization

Any reusable AI capability or tool that becomes easy to copy will decay into unmaintainable, conflicting, and insecure artifacts unless governed as a product from the start.

Ungoverned skills create a new class of technical debt including duplication, low quality, and security risks.
Open source video
Organizations must establish a centralized skills registry and governance process before scaling agentic workflows.
Open source video
Practicemedium noveltystrong evidence

Scaling skills in an AI-native organization needs a three-tier governance model: individuals create/extract skills, teams refine and collaborate on them, and a centralized platform provides a governed catalog with metadata, versioning, access control, and observability.

Why it matters

It resolves the tension between grassroots innovation (individuals extracting skills from their workflows) and enterprise control (centralized catalog and policy). This gives practitioners a concrete organizational operating model for agent capability lifecycle.

Generalization

Platform engineering for agentic systems should adopt inner-source-like progression: local creation → team curation → central governance—rather than either fully decentralized or fully top-down control.

Organizations should empower individuals to create/extract skills, enable teams to collaborate and improve them, and maintain a centralized platform for governance, cataloging, and versioning.
Open source video
Centralized platforms provide catalogs, metadata, versioning, access control, and observability.
Open source video
Mechanismhigh noveltyweak evidence

The summary points to 'progressive disclosure pattern' as a core terminology concept in skill design, suggesting that skills should reveal detail incrementally to agents in order to manage context and cost, rather than loading full instructions every time.

Why it matters

This is an operational lever for token usage and context management: skills can expose a brief description for selection, then expand detailed rules only when invoked. It is a concrete companion to the 'cost-efficient' design principle.

Generalization

Context handling in agentic systems benefits from selective materialization: give the agent minimal information to choose the right tool/skill, then progressively load the full executable procedure on invocation.

Skills make organizational know-how executable, portable, and cheap.
Open source video
Teams can reduce token usage and improve output quality by adopting standardized skills.
Open source video

Deep dives

4

Quantifying skill technical debt in enterprise agent platforms

Research question

What metrics can quantify skill technical debt and demonstrate its impact on productivity, quality, and security over time?

Why

The talk's central governance argument rests on skill debt being a first-class organizational problem; without measurable definitions of duplication, discoverability, ownership, composability, and security incidents, leaders cannot decide when centralized governance is worth its bottleneck cost.

Ungoverned skills create a new class of technical debt including duplication, low quality, and security risks.
Open source video
Organizations must establish a centralized skills registry and governance process before scaling agentic workflows.
Open source video
Source video

Semantic versioning and retirement semantics for executable skills

Research question

What semantic versioning, compatibility-testing, and retirement process can prevent a changed or deprecated skill from silently altering behavior in dependent workflows?

Why

Skills are executable, versionable artifacts, but version changes can silently change agent outputs; without library-style contracts and rollback rules, a centralized skills catalog can propagate regressions across every team that invokes a skill.

Centralized platforms provide catalogs, metadata, versioning, access control, and observability.
Open source video
Skills encapsulate best practices, conventions, and regulatory policies into structured, reusable units that agents can execute deterministically.
Open source video
Source video

Deterministic control of coding agents through outer-loop harness blueprints

Research question

How much deterministic control does a workflow/skill harness actually exercise over a coding agent's planning, tool selection, and output compared to the underlying model's autonomy?

Why

The value of skills as enterprise control units depends on their ability to shape runtime behavior; if agents can reinterpret skills or bypass blueprints, governance artifacts give a false sense of determinism and safety.

Workflows act as harness blueprints that shape the behavior of coding agents in runtime.
Open source video
Source video

Progressive disclosure mechanics for cost-efficient skill invocation

Research question

Under what retrieval and expansion conditions should skill content be progressively disclosed so agents can select the right skill without paying full context/cost for skills they do not execute?

Why

The summary claims skills are 'cheap' and 'cost-efficient' and names progressive disclosure as the pattern, but no mechanism is specified; this is the key operational lever for making a governed skill catalog practical at enterprise scale.

Skills make organizational know-how executable, portable, and cheap.
Open source video
Teams can reduce token usage and improve output quality by adopting standardized skills.
Open source video
Source video

Article ideas

3

The Real AI Product Is the Outer Loop, Not the Agent

Enterprise teams that treat the coding agent itself as the product will keep getting nondeterministic outcomes; the outer loop of workflows, skills, sub-agents, MCP servers, and hooks is the part that can be specified, versioned, governed, and tested and therefore is where engineering leverage actually lives.

Angle

Architectural argument: move attention from model/prompt tuning to controlled outer-loop composition and observability.

Source video

Stop Treating AI Skills as Prompts: They Are Executable Organizational Code

A skill is not a system prompt or a wiki page; it is a structured, versioned, testable unit of know-how. Until companies give skills IDs, owners, versions, and tests, every 'skill' is just another copy of undocumented behavior and a source of agent technical debt.

Angle

Mental-model shift from documentation to software artifact, with concrete consequences for delivery and governance.

Source video

Why AI-Native Organisations Need a Three-Tier Skill Lifecycle

Skills should not be created centrally by a platform team nor left entirely to individual experimentation; the workable pattern is individual extraction, team collaboration, and central cataloguing/policy, and the article argues why skipping any of the three tiers produces either a stagnant catalog or chaotic agent behavior.

Angle

Operating-model essay with an inner-source analogy (individual -> team -> platform).

Source video

Project ideas

4

SkillDebt Observatory

beyond-evals

Moving from copy-pasted prompt-level skills to a governed skill registry with ownership metadata and duplicate detection reduces skill duplication by at least 30% and increases agent discovery/use of existing skills, while maintaining task success rates.

Proof of concept

Simulate a 15-team product-delivery environment with identical agentic tasks. In the control phase teams share skills by copying prompts; in the treatment phase they discover and use skills via a central registry. Capture all creation, duplication, discovery, and task-run events.

Measurement

Duplicate ratio via semantic similarity of skill descriptions; owner coverage; share of task runs that resolve to a catalogued skill; task success rate; token cost per task.

Source video

Skill SemVer Gate

gatehouse

A semantic-versioning and regression-test gate on a skill registry catches at least 90% of breaking skill changes before promotion and prevents silent behavioral drift in dependent agent workflows.

Proof of concept

Create a v1 data-retention skill and a dependent workflow; introduce a breaking v2 change and a compatible v2 change. Run the workflow against both versions with and without a promotion gate that pins versions and re-runs regression tests defined by the skill owner.

Measurement

Breaking-change detection rate; semantic output difference between v1/v2 runs; count of workflow rollbacks; adoption time for safe upgrades.

Source video

Blueprint Drift Probe

movement-lab

On repeated runs of identical tasks, skills embedded in workflow blueprints produce lower inter-run output variance and more stable tool-call sequences than the same know-how delivered as an ungoverned natural-language prompt.

Proof of concept

Run two conditions on the same coding-agent benchmark: (a) instruct via system prompt only; (b) route the agent through a workflow that selects and invokes a versioned skill. Repeat each condition 20 times at temperature 0 and compare output and tool traces.

Measurement

Inter-run semantic output variance; entropy/variability of tool-call sequences; task success rate; output-to-spec fidelity.

Source video

Progressive Disclosure Comparator

new

When skills are represented as summaries plus on-invocation full rule bodies instead of full text injected into the prompt, per-task token usage drops by at least 30% without a statistically significant drop in task success across benchmark tasks.

Proof of concept

Implement a small skill registry with 10 policy skills (for example retention rules). Provide two skill selection implementations: one full-body retrieval, one summary-to-expand on invocation. Run the same benchmark tasks through an agent harness with both implementations.

Measurement

Input and output tokens per task; task success rate; rule-adherence score; cost per successful task.

Source video

Architectural implications

4

The current mental model of a single coding agent handling 'specify → design → tasks → implement' misses the complexity of enterprise delivery, which includes strategy, discovery, product development, data product delivery, and platform engineering.

Before

Teams embed instructions in individual agents or simple four-step pipelines and expect them to cover the whole lifecycle.

After

Agentic architecture must span multiple delivery lifecycles (DLCs) with separate workflows and skills per stage, coordinated through an outer loop.

Consequence

Agent systems must be modular around business stages rather than around 'coding'; platform teams need to provide skills and hooks per DLC.

Source video

MCP servers, hooks, and skills are all part of the outer workflow loop, meaning external tool access is a key part of how agents are shaped and constrained.

Before

Tool integrations (MCP servers) are treated as generic connectivity utilities unrelated to agent behavior governance.

After

MCP servers and hooks must be curated within the same skills/platform governance framework, because they mediate what agents can do and how behavioral policies are enforced.

Consequence

Access control and security policies for skills and MCP servers should be co-designed; observability over the outer loop becomes mandatory.

Source video

The governance framework implies a pyramid: individuals, teams, then centralized platform.

Before

Skill sharing is ad-hoc via prompts, copy-paste, or team wikis; there is no organizational memory of what skills exist.

After

Central registry with metadata, versioning, and access control makes skills searchable and reusable across the whole enterprise (e.g., 15 teams in the simulation).

Consequence

There must be APIs and UI for a skills registry, plus a process for promotion from local to team to platform level.

Source video

Workflows act as 'harness blueprints' that shape runtime behavior of coding agents.

Before

Agent behavior is determined mostly by the underlying model and whatever context is stuffed into the prompt.

After

Workflows are first-class code-like artifacts that deterministically guide agent execution; agents run inside the workflow harness rather than the workflow being an emergent outcome.

Consequence

Workflow definitions and skills should be versioned, tested, and reviewed like code; runtime logs should be traceable to specific workflow/skill versions.

Source video

Tradeoffs and failure modes

4

Skill governance vs. engineering velocity

Benefit

Central governance ensures reuse, quality, and security, avoiding technical debt.

Cost or risk

Centralized platforms can become bottlenecks and suppress the bottom-up innovation of individuals creating/extracting skills.

Organizations must establish a centralized skills registry and governance process before scaling agentic workflows.
Open source video
Source video

Skill specialization vs. composability

Benefit

Highly specialized skills are excellent at one task and can be reliable.

Cost or risk

Over-specialization can create fragmentation and composability gaps, making it hard to assemble end-to-end workflows across differing DLCs.

Governing skills at scale requires attention to duplication, low quality, poor discoverability, composability gaps, and maintainability issues.
Open source video
Source video

Standardized skills vs. fragmented enterprise DLCs

Benefit

Standardized, reusable skills promise cost-efficiency and deterministic outputs.

Cost or risk

The real enterprise landscape is fragmented across departments and platforms, so a one-size-fits-all platform may not fit all DLCs.

Real enterprise landscapes are highly fragmented with diverse DLCS across departments and platforms.
Open source video
Source video

Openness to agent tool use vs. prompt injection / permission risks

Benefit

Agents with many MCP servers and skills can accomplish broad tasks efficiently.

Cost or risk

Ungoverned skills create security risks and permission issues, including vulnerability to prompt injection through skill content, so access control must be at the center.

Security risks and permission issues are listed as costs of ungoverned skills, and prompt injection is listed among relevant terminology.
Open source video
Source video

Open questions

4

How should progressive disclosure be mechanically implemented for skills so that agents receive the right depth of instructions for the task without exceeding context or losing precision?

Why unresolved

The summary lists 'progressive disclosure pattern' as terminology but does not specify the retrieval, chunking, or conditional-expansion rules.

Research direction

Prototype skill structures with summary-only heads and detailed expansion arms; measure token savings, output quality, and task success across different disclosure triggers.

Source video

What metrics can quantify 'skill technical debt' and its impact on productivity, quality, and security over time?

Why unresolved

The simulation only claims to show impact; no metric definitions or baseline data are included.

Research direction

Design an evaluation benchmark that compares an organization with governed skills vs. lax skills over 6 months using duplication rate, discoverability index, owner coverage, and security incident counts.

Source video

What does the central skills platform need to enforce versioning and retirement without breaking existing workflows that depend on an older skill?

Why unresolved

Skills are executable behaviors; version changes can silently change agent outcomes, and no rollback or compatibility mechanism is described.

Research direction

Investigate semantic versioning and testing for skills, analogous to library ecosystems, plus runtime pinning of skill versions in workflow definitions.

Source video

How do heterogeneous enterprise DLCs coexist within a single centralized skills governance platform without sacrificing local context?

Why unresolved

The summary recognizes fragmentation across departments but does not resolve how central governance accommodates divergent lifecycle semantics.

Research direction

Compare federated vs. centralized registry architectures across departments to see which preserves both consistency and domain-local agility.

Source video

Key claims

6
factualVerification needed

The agentic software stack has two loops: an inner coding-agent loop and an outer loop of workflows, skills, sub-agents, MCP servers, and hooks.

Evidence

The inner loop is the coding agent harness, and the outer loop comprises workflows, skills, sub-agents, MCP servers, and hooks.

Question

Is there an accepted architectural reference implementation of the agentic software stack with these exact two loops?

Source video
causalVerification needed

Workflows act as harness blueprints that shape the behavior of coding agents at runtime.

Evidence

Workflows act as harness blueprints that shape the behavior of coding agents in runtime.

Question

How much deterministic control does a workflow blueprint actually exercise over a coding agent versus the agent's own planning?

Source video
causalVerification needed

Skills serve as blueprint harnesses that shape coding agent behavior at runtime.

Evidence

Skills serve as blueprint harnesses that shape coding agent behavior at runtime.

Question

Do skills reliably constrain agent behavior in practice, or can agents reinterpret them?

Source video
comparativeVerification needed

Skills make organizational know-how executable, portable, and cheap.

Evidence

Skills make organizational know-how executable, portable, and cheap.

Question

Compared to what baseline? Can this be measured in cost per task and task success rate?

Source video
causalVerification needed

Ungoverned skills create a new class of technical debt including duplication, low quality, and security risks.

Evidence

Ungoverned skills create a new class of technical debt including duplication, low quality, and security risks.

Question

What empirical evidence distinguishes skill technical debt from ordinary code/documentation debt?

Source video
factualVerification needed

Centralized platforms can provide catalogs, metadata, versioning, access control, and observability.

Evidence

Centralized platforms provide catalogs, metadata, versioning, access control, and observability.

Question

Which recent platforms actually implement all these functions for agent skills?

Source video

Connections

5