{"slug": "every-enterprise-will-have-its-own-ai-harness", "title": "Every Enterprise Will Have Its Own AI Harness", "summary": "A developer argues that every large enterprise will adopt a shared AI harness—an open-core runtime configured with identity, model gateways, connectors, policies, and audit—to replace the current pattern of building dozens of overlapping internal AI applications. The harness standardizes governance and infrastructure, with plugins for enterprise-specific capabilities, while keeping policy enforcement deterministic and external to the model.", "body_md": "I think every large enterprise is going to have its own AI harness.\n\nNot necessarily a proprietary harness built from scratch. More likely, it will be an open core that the enterprise configures with its own identity systems, model gateways, internal tools, data sources, policies, approval processes, and audit requirements.\n\nThat harness could become the shared runtime behind a large part of the company’s internal AI work.\n\nThis matters because the current pattern does not scale well. A team wants an internal assistant, so it builds an application. Another team wants a policy search tool, so it builds a second application. Security wants a review tool. Support wants a ticket assistant. Engineering wants a code and Jira assistant. Each project ends up rebuilding some version of model access, authentication, connectors, permissions, logging, and policy checks.\n\nAfter enough projects, the company has dozens or hundreds of small AI applications with overlapping infrastructure and different security assumptions.\n\nI believe the enterprise harness is how we get out of that cycle.\n\nI do not mean one giant chat window that replaces every internal application.\n\nTeams will still build applications, workflows, command-line tools, and specialized interfaces. The difference is that those products can become thinner. Instead of every application building its own integration and governance layer, it can use a shared enterprise harness.\n\nThe harness becomes the place where the company standardizes the hard parts:\n\nThe applications sit above that shared layer.\n\n```\nInternal apps, developer tools, agents, and experiments\n                         ↓\n                Enterprise AI harness\n  ┌─────────────────────────────────────────────────┐\n  │ Identity  Model gateway  Connectors  Approvals │\n  │ Policy    Security checks  Audit     Cost rules │\n  └─────────────────────────────────────────────────┘\n                         ↓\n   Jira  Confluence  GitHub  ServiceNow  Slack  Data\n```\n\nThis is the same reason enterprises standardize cloud platforms, identity providers, API gateways, and CI systems. A shared foundation reduces repeated work and gives security teams a consistent control point.\n\nThe plugin model is what makes this idea practical.\n\nA generic open-source harness cannot know an enterprise’s internal project structure, risk model, cybersecurity policies, model providers, or approval process. It should not try to know them.\n\nInstead, the enterprise adds plugins and providers for the capabilities it needs.\n\nFor example:\n\nThis is more useful than building a separate application for every combination of these capabilities.\n\nOnce the capability exists as a governed plugin, many internal applications can use it. A developer portal, a support workflow, a security review assistant, and a command-line tool might all use the same Jira connector and the same policy checks.\n\nThere is an important technical distinction here.\n\nA plugin that reads company policy can help an employee understand the policy. It can answer questions, cite the source, show the effective date, and explain which review is required.\n\nThat is not the same as enforcing the policy.\n\nEnforcement should be deterministic and external to the model. If the policy says only projects `ENG`\n\nand `SEC`\n\nare allowed, the harness should deny any other project through code or a policy-decision service. The model should not be asked to interpret its way around the rule.\n\nIn other words, an enterprise harness needs both:\n\nThis separation is one of the most important parts of the architecture. It gives employees useful context while keeping authorization outside the conversation.\n\nThey do not all disappear, and they should not.\n\nA sales application and a cybersecurity review tool have different users, workflows, interfaces, and measures of success. They may still need separate products.\n\nWhat can disappear is a large amount of duplicated backend work.\n\nEach application should not need to invent its own way to:\n\nWith a shared harness, an application can focus on the job it was built to do.\n\nThis also changes experimentation. A team can test a new use case against mock connectors and audit-only policy. If the idea proves useful, it can move toward controlled access, approval-gated writes, and production monitoring without replacing the entire foundation.\n\nThe path from experiment to production becomes a profile and control change, not a full rewrite.\n\nImagine an engineer asks an internal tool to review a proposed service and open any required work items.\n\nThe application could use one shared harness to:\n\nThe user sees one coherent workflow. Underneath it, each capability has a narrow contract and a clear owner.\n\nSecurity can maintain the policy and review plugins. The platform team can maintain identity, model routing, and audit. Application teams can compose those capabilities without copying them.\n\nThat is the part I find most interesting. The harness is not only a tool runner. It becomes a way to encode how the enterprise wants AI-enabled work to happen.\n\nDeepSeek recently open-sourced its Harness. I cloned it, studied the Cordis plugin architecture, installed it locally, and built an enterprise suite on top of it.\n\nThe technical milestone is not simply that another coding agent is available. The important part is that the harness itself is open and composable.\n\nAn enterprise can inspect the runtime, build its own plugins, create different profiles, replace providers, and keep internal controls outside a vendor-managed black box. That makes it possible to test this enterprise-harness model with real code today.\n\nI released my implementation as the [DeepSeek Harness Enterprise Suite](https://github.com/mickymultani/deepseek-harness-enterprise-suite).\n\nThe first release composes three plugin roles:\n\n```\nDeepSeek Harness profile\n  ├─ enterprise-atlassian\n  ├─ enterprise-policy\n  └─ enterprise-audit\n```\n\nA protected tool request follows this path:\n\n```\nTool request\n  → validate arguments\n  → evaluate policy\n  → apply project, space, and DLP guards\n  → request approval when required\n  → resolve credentials\n  → call the enterprise API\n  → validate the structured result\n  → verify the outcome\n  → record the final redacted audit event\n```\n\nConnectors do not decide enterprise authorization. Policy plugins do not hold vendor credentials. Audit observes the final outcome rather than the agent’s original intention.\n\nThat separation lets an enterprise replace any layer without rewriting every tool.\n\nVersion `0.1.0`\n\ncurrently provides:\n\nThe suite starts in mock mode. Installing it cannot modify a real Atlassian tenant until an operator explicitly enables live mode and configures credential references.\n\nFrom a DeepSeek Harness source checkout:\n\n```\nexport DSH_HOME=/absolute/path/to/an/evaluation-home\npnpm dsh plugin --profile web add github:mickymultani/deepseek-harness-enterprise-suite#v0.1.0\npnpm dsh --profile web --dump-config\npnpm dsh web --no-open\n```\n\nTo scaffold a new connector project:\n\n```\nnpx dsh-enterprise-suite create-dsh-enterprise-plugin knowledge-search\n```\n\nThe roadmap covers more than application connectors.\n\nIt includes identity propagation, Vault and cloud secret providers, OPA and Cedar policy decisions, model gateways, OpenTelemetry, SIEM export, enterprise search, ServiceNow, GitHub Enterprise, security operations, cloud controls, legal, privacy, vendor risk, finance, HR, and customer support.\n\nThe order matters:\n\nA connector that can call an API is not automatically ready for enterprise production. It also needs ownership, authorization, data handling, failure behavior, testing, audit, and incident procedures.\n\nIf this pattern works, enterprises gain more than technical reuse.\n\nThey gain a standard place to answer important questions:\n\nThat gives platform, security, risk, and application teams a common surface.\n\nIt also gives leadership a clearer view of experimentation. Instead of discovering untracked applications after they are already in use, the company can provide a supported path for teams to build and test ideas with approved models, connectors, and controls.\n\nI think open, plugin-based harnesses will become a standard part of the enterprise technology stack.\n\nCompanies will not all use the same plugins or policies. That is the point. Each enterprise will shape the harness around its own systems, risk tolerance, regulatory obligations, and ways of working.\n\nThe shared core provides the runtime. The internal plugins make it the company’s runtime.\n\nThat is why I see the open-sourcing of DeepSeek Harness as a meaningful milestone for enterprise AI. It creates a practical foundation for moving from scattered experiments and duplicated internal applications toward a governed, reusable platform.\n\nThe project is open source under the MIT license:\n\n[github.com/mickymultani/deepseek-harness-enterprise-suite](https://github.com/mickymultani/deepseek-harness-enterprise-suite)\n\nI would like to hear how other platform, security, and engineering teams see this. Will enterprises converge on one internal harness? Which capabilities need to be standardized first? And where should the boundary sit between the shared harness and the applications built on top of it?", "url": "https://wpnews.pro/news/every-enterprise-will-have-its-own-ai-harness", "canonical_source": "https://dev.to/mickymultani/every-enterprise-will-have-its-own-ai-harness-1gfi", "published_at": "2026-08-25 01:30:42+00:00", "updated_at": "2026-08-25 01:43:18.734479+00:00", "lang": "en", "topics": ["artificial-intelligence", "ai-infrastructure", "ai-agents", "ai-policy", "developer-tools"], "entities": ["Jira", "Confluence", "GitHub", "ServiceNow", "Slack"], "alternates": {"html": "https://wpnews.pro/news/every-enterprise-will-have-its-own-ai-harness", "markdown": "https://wpnews.pro/news/every-enterprise-will-have-its-own-ai-harness.md", "text": "https://wpnews.pro/news/every-enterprise-will-have-its-own-ai-harness.txt", "jsonld": "https://wpnews.pro/news/every-enterprise-will-have-its-own-ai-harness.jsonld"}}