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A skill-driven enterprise separates two things most AI agent projects mix together: the reusable business know-how that says how work should be done, and the agents that carry out tasks using that know-how. In the architecture proposed by Manovikas Muduganti in a DEV Community article dated September 16, 2026, a central platform called the agent harness decides what each agent may access, which checks apply, and whether the result is acceptable. The model is the author’s design proposal, not an established enterprise standard or a proven operating model. This guide explains how the pieces fit, where the governance guidance from NIST fits alongside it, and what you would need to evaluate before adopting any part of it.
The core idea: separate know-how from execution
The proposal rests on one design separation. Business logic, context, expected outputs, and quality criteria live in reusable packages called skills. Agents are the runtime workers that apply a skill to a specific request. Because the skill holds the knowledge, a team can change how a process is done in one place rather than rewriting every agent that performs it.
The author frames the whole model as a chain: Intent → Skill → Agent → Governed Execution → Verified Outcome. A more detailed orchestration sequence adds identity, context, prerequisites, policy, and evaluation steps, covered below. Both are explanatory models from the author, not industry-wide terminology.
The building blocks
Business skills
A skill, in this model, bundles instructions, business knowledge, decision logic, required context, expected outputs, required tools or capabilities, policies, and criteria for judging whether the work was done well. The distinction the author draws matters in practice: a skill describes how capabilities are applied to a meaningful business task, while a tool supplies a single capability, such as searching documents or retrieving a customer record. A refund-handling skill, for example, would state the eligibility rules, the approval thresholds, and the output format. The search and record-lookup functions it calls would be tools.
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The skills marketplace
The proposal includes a place to publish, discover, reuse, version, test, and improve skills. The author describes this as a design element rather than a product that exists today, and does not name an existing marketplace or claim one is widely deployed. Treat it as an internal catalogue pattern to be built or bought, not something you can adopt off the shelf based on this article.
The agent harness
The harness is the surrounding platform that does the governing. According to the proposal, it interprets the user’s intent, selects a skill, checks prerequisites and user access, assigns the capabilities an agent is allowed to use, applies policy, routes anything needing approval, executes the task, and evaluates the result. The author’s central governance principle is that an agent should not determine its own permissions. Whatever the agent is allowed to do is granted from outside it.
MCP and enterprise capabilities
The Model Context Protocol (MCP) appears in the proposal as a standardized way to reach enterprise systems and tools. The harness decides which of those capabilities an agent receives. Keep MCP’s role narrow: in this model it is an access mechanism. The article does not show that MCP by itself handles identity management, policy enforcement, or risk governance. Those responsibilities sit with the harness and the organization around it.
Task-specific agents
Rather than maintaining a permanent agent for every business function, the author suggests assembling an agent when work arrives. An assembled agent is built from an agent template, a skill, the relevant context, the MCP capabilities it is permitted to use, applicable policies, and evaluators that check its output. This is presented as a direction the author favours, not a tested result.
How a request moves through the system
The detailed orchestration sequence in the proposal runs in this order. Each step is a gate: a failure at any point stops the work or routes it for review rather than letting the agent improvise.
- Intent. The harness interprets what the person or system is asking for.
- Identity. The requester is authenticated, and their access rights are determined.
- Context. Relevant business data and background are gathered within what that identity may see.
- Skill. The harness selects the skill that matches the intent.
- Prerequisites. The harness confirms the required context, tools, and capabilities are present before work begins.
- Agent. A task-specific agent is assembled, or an existing one is assigned.
- Policy. Policies are applied, including any approval requirements.
- Execution. The agent performs the task using only the capabilities granted to it.
- Evaluation. The output is checked against the skill’s criteria before it is treated as complete.
A useful way to test your understanding of the sequence is to ask where a human would be required to approve. In the proposal, approval is a policy outcome, so it can be triggered at step 7 for certain actions, such as one above a dollar threshold, without changing the skill itself.
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Lifecycle: how skills are built and kept current
The proposal treats skills as software that needs a life cycle: Create → Test → Publish → Observe → Improve. Testing includes structural checks and permission checks, plus realistic scenario evaluation. The author asks teams to judge several things about an agent working a skill:
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- Does it follow the skill’s instructions?
- Does it use suitable information?
- Does it stay within its permissions?
- Does it escalate when it should?
- Does it produce useful output?
The observe and improve stages are what keep a skill from going stale. When monitoring shows repeated escalations or failed evaluations, the fix belongs in the skill, which then benefits every agent that uses it.
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Governance: where NIST fits
The proposal is an architecture, not a compliance framework. For governance context, the most widely referenced general guidance is the NIST AI Risk Management Framework (AI RMF), which NIST describes as voluntary guidance for incorporating trustworthiness into the design, development, use, and evaluation of AI systems. It is organized into four functions. NIST’s January 26, 2023 announcement quotes NIST Director Laurie E. Locascio saying the framework “can help companies and other organizations in any sector and any size to jump-start or enhance their AI risk management approaches.” That is NIST’s statement of intended usefulness, not an independent evaluation of any particular architecture. NIST also reports that more than 240 organizations across private industry, academia, civil society, and government took part in developing the framework. That figure describes who helped write it, not how widely it has been adopted.
The AI RMF is not a validation of the skill-driven model, but its functions map cleanly onto the harness design:
- Govern: define policies, accountabilities, and roles, including the human oversight the harness routes to. NIST’s core explicitly covers defining and differentiating roles and responsibilities for human-AI configurations.
- Map: document each skill’s intended purpose, users, context, assumptions, and potential impacts before deciding to deploy it.
- Measure: evaluate security, resilience, and other relevant risks, test before deployment and regularly in operation, and document the methods and results. The skill lifecycle’s evaluation stage is the natural place for this.
- Manage: prioritize assessed risks, decide whether a deployed system meets its objectives, and plan responses and continued monitoring.
NIST has said that AI RMF 1.0 is being revised, so check the NIST AI Resource Center for the current version before citing a specific revision in a governance document. Tailor measures to the intended use, your risk tolerance, and the deployment context. A low-stakes internal summarising skill and a skill that can change customer records do not need the same controls.
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The proposal’s most practical question is whether to keep one long-lived agent per business function or assemble agents per task. The sources do not compare the two with performance data, so the table below lists the decision axes and what the proposal does and does not establish for each.
| Decision axis | Permanent specialist agents | Task agents assembled from skills | What the proposal establishes |
|---|---|---|---|
| Reuse of business logic | Logic often lives inside each agent | Logic lives in reusable skills | Skills are proposed as reusable; no measured reuse rate is given |
| Permission enforcement | Depends on how each agent is configured | Granted by the harness at assembly time | The harness, not the agent, assigns permissions; no comparison data |
| Prerequisite handling | Not stated | Checked by the harness before execution | Prerequisite checks are part of the sequence; no failure-rate data |
| Test coverage and evaluation | Not stated | Maintained per skill through the lifecycle | Evaluation criteria are proposed; no coverage figures |
| Observability and versioning | Not stated | Versioned skills, observed in operation | Versioning and observation are proposed; no tooling is named |
| Maintenance effort | Not stated | Effort shifts to maintaining templates and skills | No effort or cost estimate is given |
Evidence limits to keep in view
The proposal is one author’s design, published as an article rather than a standard or a controlled evaluation. It does not establish adoption rates, productivity gains, cost reductions, or comparative performance for business skills, skills marketplaces, agent harnesses, or task-specific agents. Treat the benefits it describes, including faster assembly of agents and easier reuse, as design goals to test in your own environment, not as results that will carry over to your organization.
Quick Recap
A practical way to pilot the model
- Pick one process with clear rules and a bounded set of tools, such as routing internal IT requests.
- Write the skill first: intent, decision logic, required capabilities, output format, and pass/fail criteria.
- Decide which actions need approval and encode those as policies outside the agent.
- Grant the minimum set of capabilities, and confirm the agent cannot widen them itself.
- Build evaluation scenarios that include failure cases and escalation cases, not only success paths.
- Record decisions and results against the NIST Govern, Map, Measure, and Manage functions.
- Only then consider a second skill or a shared marketplace.
Sources
- Manovikas Muduganti, “The Skill-Driven Enterprise Bridging Intent and Execution with Governed AI Agents,” DEV Community, September 16, 2026: https://dev.to/vikas_mano_870c09cfee793f/the-skill-driven-enterprise-bridging-intent-and-execution-with-governed-ai-agents-4d69
- NIST AI Resource Center, “AI RMF Core”: https://airc.nist.gov/airmf-resources/airmf/5-sec-core/
- NIST, “NIST Risk Management Framework Aims to Improve Trustworthiness of Artificial Intelligence,” January 26, 2023: https://www.nist.gov/news-events/news/2023/01/nist-risk-management-framework-aims-improve-trustworthiness-artificial
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