Agentic AI as Universal Interface

Software is distilled expertise. Two barriers have prevented most expertise from becoming software: coding skill and system access knowledge. Agentic AI removes both.

Software is distilled expertise in its most executable form.

When an engineer encodes a structural calculation into a spreadsheet, that spreadsheet embeds what the engineer knows. When a business analyst creates formulas to process financial data, those formulas distill how the business works. When a software engineer encodes a business process into an ERP system, that system inscribes the process designer’s expertise into reusable automation.

The calculation runs without the engineer present. The formula executes without the analyst’s involvement. The ERP system processes transactions without the process designer’s intervention.

Software is expertise made runnable. Human knowledge, encoded so precisely that a machine can execute it. The entire software industry exists to distill what people know into instructions machines can follow.

The hard part has never been making computers do things. Computers follow instructions well. The hard part is distilling what you know into instructions a computer can follow.

Two Barriers

Historically, two barriers have prevented most expertise from becoming software.

Barrier 1: Coding expertise required. To encode your knowledge, you had to know how to program. Domain experts — the people who actually understood the work — needed developers as intermediaries. The developer didn’t have the domain knowledge. The expert didn’t have the coding skill. Translation was lossy. Communication was expensive. Most expertise stayed locked in human heads.

Barrier 2: System access knowledge required. Even if you could code, you had to know how to access systems. Where’s the data? What format is it in? What sequence of operations retrieves it? How do you connect to this service? What authentication does it require? Each system demanded specific technical knowledge to use programmatically.

These barriers meant that expertise distillation was rare and expensive. Most knowledge emerged only through manual work that couldn’t scale.

What Agentic AI Changes

Agentic AI removes both barriers.

Barrier 1 falls. The agent generates code from natural language. You describe what you want; the agent writes the instructions. No coding skill required. The domain expert can distill their own expertise directly, without a developer intermediary. This is the practical application of tacit space shrinkage: knowledge that was locked because the expert couldn’t code becomes expressible through natural language.

Barrier 2 falls. The agent figures out how to access systems. Given permission to access a data store, the agent examines its structure and writes the instructions to retrieve what you need. Given credentials for a service, the agent reads documentation, determines the correct operations, and generates the connection. The technical knowledge of how to access systems becomes the agent’s job.

What remains: Domain expertise. Access permissions. The ability to specify what you want. The judgment to verify results are correct.

What shrinks: The intermediary layers. The developers translating between expert and machine. The integration specialists connecting systems. These roles don’t vanish, but the volume of work requiring them contracts as direct encoding becomes feasible.

The agent is persistent. Agentic AI isn’t a single-shot translation. The agent retries when something fails. It adapts when the first approach doesn’t work. It explores documentation, examines error messages, adjusts its method. Given time and tokens, it will figure out how to accomplish most of what you ask — and what’s possible today is less than what will be possible tomorrow. The barrier to accessing systems shifts from skill to patience, and agents are patient.

The Universal Interface

Agentic AI connects domain expertise directly to accessible systems. The more systems it can reach, the more universal the interface becomes.

Data storage with discoverable structure becomes directly usable. You don’t need someone to build an application on top of it. Describe what you want; the agent writes the instructions to retrieve it.

Services with documented interfaces become directly usable. The agent reads documentation, figures out the connection, executes the operations. No pre-built integration required. The scope of “discoverable” and “documented” expands as agent capabilities improve.

The barrier shifts from technical (coding, system knowledge) to cognitive (specification, verification) and administrative (data access permissions).

Systems with explorable interfaces become accessible to anyone with permission and an agent. The interface doesn’t need to be documented — it needs to be explorable. Given time, the agent can figure out how most systems work, and the set of systems it can handle grows as capabilities improve. Computer instructions can express any computation. Natural language can express intent. The agent bridges the gap.

What This Requires

For agentic AI to function as universal interface, systems must be accessible:

• Interfaces must be discoverable (documentation helps but isn’t required)
• Connections must be permitted
• Access credentials must grant real capability

The trend toward machine-readable interfaces accelerates this. Systems designed for developers are also accessible to agents. Systems designed for automated access are more accessible still.

Authentication will evolve. Current authentication complexity exists largely because human users are security vulnerabilities — they reuse passwords, fall for phishing, lose credentials. When access is programmatic rather than human-mediated, authentication can be simpler and more secure: rotating keys, certificate-based auth, machine-to-machine protocols. Removing the human from the access path removes a major source of security vulnerability. Authentication systems built for human users will give way to authentication systems built for agent access.

The infrastructure decisions made today determine how accessible systems will be to this mode of interaction tomorrow.

The Remaining Human Contribution

Agentic AI as universal interface concentrates rather than eliminates human contribution:

Domain expertise. What should happen? What are the rules? What are the exceptions? The knowledge to be distilled remains human. The agent encodes; the expert knows what to encode.

Specification of intent. The clearer the specification, the better the generated approach. Articulating what you want with precision is a skill. Domain expertise becomes specification expertise.

Verification of method. Does the generated approach do what you intended? This doesn’t require reading code. Translation is bidirectional: the same agent that generated the code can describe what it does. “Explain exactly what this code does.” “What could go wrong?” “What assumptions does this make?” The agent has no memory of having written it and no ego invested in defending it. Adversarial review by the same tool that generated the artifact is effective verification. The domain expert confirms the description matches their intent — a judgment they’re equipped to make.

Judgment in novel situations. When the agent doesn’t know how to proceed, when the situation is ambiguous, when competing considerations require tradeoffs. Judgment remains human.

The human contribution contracts to: knowing what should happen, specifying it clearly, confirming the approach is right, handling what the agent cannot. The rest is increasingly generated — and the scope of “the rest” expands over time.

Objections

“If users can’t code, they can’t verify generated code.” They don’t need to author code. But verification requires more than AI explanation alone. Research shows AI explanations, like reasoning traces, can be unfaithful — the agent’s description of what the code does may not accurately reflect what it actually does. Verification needs some check independent of the generating model: human review, automated testing, secondary models, or domain-specific validation. Reverse translation is part of a verification protocol, not a substitute for one. [See Answers to Critics for why this matters.]

“Specification is as hard as implementation — you still need to handle edge cases.” Specification is a skill, but a mental skill rather than a technical one. Domain experts already specify precisely when instructing subordinates. The same skill applies to instructing an agent. The agent can be configured to demand specificity, to ask clarifying questions rather than assume. Ambiguous specifications get caught before generation, not after. And the premise that the agent needs the expert to specify every edge case understates what frontier models know. These models have broad domain knowledge across fields — not expert-level in every domain, but often sufficient to catch common gaps and ask clarifying questions. The agent helps because it knows something about the domain, and what it knows improves continuously.

“Business logic is tribal knowledge, undocumented and contradictory.” True. But the interface is the AI tool itself. Describe the logic once to the agent; the agent encodes it. That encoding becomes available to every other agent in the organization. No user training, no communication cascade, no documentation that drifts from reality. The distribution problem disappears because distribution is automatic — express once, encoded for all. The hard part becomes getting the expert to articulate their knowledge one time.

“Rate limits and API quotas constrain what agents can do.” These are implementation details. The cost of tokens and bandwidth is negligible against the value of direct system access. Rate limits are speed bumps, not roadblocks. Agents are patient. Organizations with scale concerns can self-host models sized to their peak load.

“Some systems are deliberately obfuscated — proprietary formats, undocumented protocols, binary interfaces.” Deliberate obfuscation is currently a hard barrier — it blocks discoverability. But obfuscation as a durable strategy only works when there’s not much value behind it. If there’s real value, customers will choose more open alternatives, and competitors will build replacements. The cost to build alternatives is collapsing. Obfuscation buys time, not permanent protection. Legal barriers (anti-circumvention law, terms of service) can extend the window, but they don’t close it. Organizations will remember who tried to extract rent through obstinate refusal to enable access. The pressure is toward openness: systems that remain obfuscated will be routed around, not accommodated.

What Must Be True for This to Be Safe

Agentic AI as universal interface creates new attack surfaces. For this pattern to be deployable in professional contexts, constraints must be architectural:

• Least-privilege permissions. Grant only what the task requires. Read-only by default for exploratory work.
• Explicit approval gates. Any write action requires human confirmation, not just initial permission scoping.
• Action logging. Every tool invocation recorded with inputs, outputs, and the decision path that led to it.
• Context separation. Strict boundaries between confidential contexts. One project, one agent context.
• Secure secrets handling. Keys, tokens, and credentials managed through secure channels, not exposed in prompts or logs.

The universal interface thesis depends on these controls being architectural, not advisory. The same persistence that makes agents valuable—they keep trying until they figure out how systems work—makes them dangerous without constraint.

There’s a deeper governance issue here. Agentic frameworks have automated the routing decision—whether to answer a query through code or provide a direct response. When the AI makes that choice autonomously, the human has lost control over the oracle/assistant distinction itself. [See Update for February 2026 for how this “agentic erosion” creates new governance gaps.]

Connection to the Framework

Agentic AI as universal interface is what makes Executable Knowledge Architecture scale.

EKA describes the pattern: expert intent → AI translation → executable artifact → human verification → reproducible results.

The universal interface explains the reach: the executable artifact can interact with any accessible system. The expert’s knowledge can be distilled into instructions that touch any data, any service, any platform the agent can learn to use.

The artifact — the generated code — is inspectable. You can read it. You can verify it does what you intended. You can run it again and get the same results. The methodology is exposed. Defensibility is achievable.

Patterns accumulate. Generated artifacts aren’t disposable. Useful patterns get identified, vetted, and made available for reuse. The Ontology Generation process describes how: logs are mined for recurring patterns, successful approaches are extracted and validated, reusable artifacts are distributed across the organization. What starts as ephemeral generation becomes curated organizational knowledge. The one-off becomes the template.

→ See Automating Expertise Gets Easier and Easier for what this means as capabilities improve.