The most interesting AI work happening inside enterprise businesses today isn't chatbots (despite being what everyone talks about!), and it isn't fully autonomous agents booking flights and signing contracts on someone's behalf. It's somewhere in the middle: orchestrated systems where AI does the cognitive work it's genuinely good at (drafting, classifying, summarizing, deciding between options) while traditional software handles the parts that need to be predictable, auditable, and fast.
We've been building these systems for clients across regulated industries, marketing organizations, and editorial operations, and a pattern has emerged. The companies getting real leverage from AI aren't the ones chasing fully autonomous agents. They're the ones building agentic applications: workflows where AI participates as a controlled, governed component inside a larger orchestrated system.
This post, which I've been meaning to write for a long time, is about how those applications get built, and what separates the ones that ship from the ones that stall in a proof-of-concept demo.
What Are Agentic Applications?
Agentic applications sit between two extremes. On one end, you have single-shot AI tools: a chatbot, a document summarizer, a single API call wrapped in a UI, or AI Wrapper Applications. On the other end, you have fully autonomous agents that plan, execute, and self-correct with minimal human input: these are particularly newsworthy as of late. Both have their place, but neither fits the way most enterprise businesses actually operate.
What works in practice is orchestrated AI: applications where multiple AI calls are chained together inside a workflow that the business still controls. The orchestration layer enforces sequence, validates output, escalates exceptions to humans, and maintains an audit trail. AI isn't running the show, but rather it's a powerful contributor inside a system that still has rules.
There's also a second thought worth being clear about: individual efforts versus collaborative output. A lot of the AI tooling getting the most attention right now is built to accelerate individuals. Claude Cowork is a strong example, a desktop tool that helps a single person move faster through their own work. That's genuinely valuable, but it isn't what agentic applications do. If ten people on a team each adopt an individual AI assistant, you get ten people doing better individual work, and ten different outputs. Solving for organizational consistency requires a different kind of system: one where the AI participates in a shared workflow with shared rules and shared standards, not one where each user has their own assistant making their own choices.
Governance matters more than it sounds like it does. In regulated environments (financial services, healthcare, anything touching customer data), you can't hand a workflow to a model and hope it does the right thing. You need governance: clear boundaries on what the AI can decide, where humans must intervene, what gets logged, and how outputs get validated before they affect anything downstream. Agentic applications give you that governance while still capturing most of the upside of AI participation.
How They Work
Under the hood, an agentic application is mostly traditional software. Routing, queueing, state management, user interfaces, data persistence, authentication. None of that gets reinvented. What changes is that certain steps in the workflow, instead of being handled by deterministic code, are handled by stateless AI transactional components.
"Stateless" is the important word. Each AI call should be designed as a discrete transaction: it takes structured input, applies a prompt, returns structured output, and that's the end of its existence. State lives in the application layer, not in the model. This is what makes the system testable, retryable, and observable. It allows developers to build the functions separately, in case full automation and reuse later becomes desired. It also makes it possible to swap models without rewriting the application.
Multi-LLM capability falls out of this design naturally. If your AI calls are stateless transactions, you can route different tasks to different models based on cost, capability, or latency. A classification task might go to a small fast model. A nuanced editorial decision might go to a frontier model. A vision task might go to a different provider entirely. The application doesn't care; it just knows it sent input X and got back output Y in a defined shape.
The same logic applies to non-AI components. Text scraping might be handled by ScrapingBee. Image processing might run through a dedicated service. PDF generation might happen on a machine sized for that workload. These are all interchangeable. Good agentic architecture treats every external dependency (AI or otherwise) as a swappable component with a clean contract.
The piece that often gets underweighted is prompt management. In a serious system, prompts are first-class artifacts. They're versioned, tested, compared against each other, and deployed like code. They live in a registry, not scattered across source files. When a prompt changes, you know what changed, who changed it, and which workflows are affected. Without this discipline, your application becomes impossible to debug the moment something goes sideways in production.
The AI Component
What determines whether the AI part actually works is the quality of the prompts and the context being fed into them.
A useful prompt for a business workflow is almost never just instructions. It's instructions plus context: the relevant business rules, the policies that apply, examples of correct output, and, most importantly, the specific information the AI needs to make this particular decision. That information usually comes from one of two places. Either it's retrieved at runtime from a knowledge base via RAG, or it's injected directly from upstream workflow state.
RAG is the right pattern when the relevant context lives in a library too large to fit in a single prompt: documentation, knowledge base articles, prior cases, policy libraries. The system queries that knowledge base, pulls the most relevant chunks, and assembles them into the prompt at call time.
Direct context injection is the right pattern when the relevant context is already known: a customer record, the output of a previous workflow step, a structured business rule that applies to this specific case. Most agentic workflows use both, and the orchestration layer is what coordinates them.
The hard part is rarely the model. The hard part is figuring out exactly what context a given decision needs, structuring it cleanly, and writing a prompt that gets reliable output the first time.
Typical Features
Across the agentic applications we've built, a handful of components show up again and again. They aren't the entire system, but they're the parts most operations-focused builds end up needing.
Document repositories. Most workflows depend on reference material that needs to be available during execution: policy documents, style guides, prior examples, regulatory text, brand standards. The application needs a place to store this material in a structure that can be queried by the AI at runtime. This is where RAG implementations live, and it's where a lot of quiet leverage comes from. The AI's outputs are only as good as the reference material it can see, and a well-organized repository is the difference between an assistant that knows your business and one that's making educated guesses.
Scoring agents. Many workflows need an AI component that evaluates work against predefined rules, templates, or quality standards. These scoring agents run during or after a workflow step and produce a structured assessment: pass or fail, a numeric score, specific flags for what was missing or out of standard. They're useful both as quality gates and as feedback mechanisms for users, and they're often the easiest piece to build well because the criteria are explicit.
Live assistants. Where scoring agents evaluate finished work, live assistants participate as the work happens. They review inputs as users type or select, surface suggestions in real time, flag inconsistencies with established standards, and propose corrections before anything gets submitted. The design challenge here is latency and interruption: the assistant has to be fast enough to feel responsive and quiet enough not to derail the user. It need not be a chatbot, by the way. It can instantly provide feedback as fields are completed or before subsequent steps are executed.
Management and performance tools. Once an agentic application is running across a team, the data it produces becomes valuable on its own. Which users are scoring well? Which are repeatedly tripping the same flags? Where in the workflow are people slowing down? Dashboards that surface this information turn the application into a coaching tool. The same AI that's helping users do better work in the moment is also showing managers exactly where to focus their training time.
Why Build, Not License
The market is full of licensed tools promising to deliver agentic capabilities out of the box. Drop in a SaaS subscription, configure a few workflows, and supposedly you have a working application. For some lightweight use cases, that's fine. For anything that touches sensitive data, regulated processes, or enterprise governance, it falls apart fast.
The core issue is visibility. When you license a tool, you don't control what's happening at the system prompt level. You don't know what instructions the underlying LLM is being given, what context is being injected, what filtering is or isn't in place, or what gets logged where. You're trusting the vendor's governance to be at least as strict as your own, silently, on every prompt, every call, every workflow run.
For enterprise buyers, that's often the deal-breaker. Many of our enterprise clients have already invested in their own governance layer: a wrapper around an LLM that enforces top-level policies, redacts sensitive information, manages model selection, and exposes the result through an internal API. When we build an agentic application on top of that API, the application inherits every guardrail the organization has already put in place. The system prompts, the routing logic, the logging, all of it is visible and auditable because we wrote it. When you license a SaaS tool, you're bypassing the work the organization has already done and trusting an outside vendor's invisible defaults instead.
This is the strongest argument for building rather than licensing, and it gets stronger the more regulated or sensitive the workflow is. Custom-built agentic applications let you choose your models, define your own system prompts, integrate with your existing governance, log everything, and audit anything. Licensed tools, by design, hide most of that from you.
Stages of Development
Building one of these systems is a multi-stage process. Each stage exists to reduce a specific kind of risk.
Discovery
Discovery is about finding the right workflow to automate. Not every workflow benefits from AI orchestration. The best candidates share a few traits: they happen often enough that automation has leverage, they suffer from inconsistency across people or instances, and they involve cognitive work that humans are doing slowly or unevenly. Discovery is usually a series of conversations with the people doing the work today, identifying where time is being lost and where outputs vary in ways the business doesn't actually want.
One pattern we've seen repeatedly: a client has a critical operational process running on a Word template or an Excel sheet with defined fields, and on paper, the structure should produce consistent output. In practice, every employee fills it out a little differently. The fields are the same, but the content varies enough that downstream consumers (other teams, reporting systems, auditors) can't treat the outputs as interchangeable. The organization has a process. What it doesn't have is consistency.
We worked on a project that addressed exactly this. The fix wasn't replacing the existing tooling. The fix was adding an AI scoring component that evaluated each entry as users worked through it, flagging where their phrasing, level of detail, or structural choices were going to create problems downstream. The AI didn't write the content for users. It guided them toward the organizational standard. The output of that project was the same operational process the client had always run, except now every instance of it actually matched.
Prototype
Prototype is where the idea earns the right to keep going. A good prototype demonstrates compounding AI orchestration: a chain of prompts where each one operates on the output of the previous one. This is the moment where stakeholders see that AI isn't just doing one task; it's running a sequence: extract, classify, draft, refine, validate. The prototype doesn't need to be production-grade. It needs to make the leverage obvious. If a prototype can't do that in a fifteen-minute demo, the underlying idea probably isn't strong enough to take forward.
Development
Development is the unglamorous stage where the prototype becomes a real system. State management gets formalized. Error handling becomes comprehensive. Monitoring and observability are built in from day one rather than bolted on. The prompt registry gets stood up. Authentication, authorization, and audit logging become real concerns. This is also where the non-AI infrastructure (queues, retries, idempotency, rate limiting) does the heavy lifting that keeps the AI calls reliable when traffic and edge cases show up.
Revision and Iteration
Revision and iteration is the stage that never ends. The system gets better over time because the prompts get better over time, and the prompts get better because real usage exposes edge cases the team didn't anticipate. Good agentic applications have feedback loops built in from the start: user corrections, output ratings, automated evaluations against held-out examples. Every one of these feeds back into the prompt registry and improves the next version of the workflow. This is where compounding really shows up, not just inside a single run, but across months of refinement.
Closing
The companies pulling real value out of AI right now aren't the ones with the most ambitious agents. They're the ones who picked a specific, painful, recurring workflow, broke it into discrete cognitive steps, wrapped those steps in orchestration they can govern, and committed to iterating on the prompts indefinitely. That's what an agentic application is. It's less impressive on a demo reel than autonomous agents, and considerably more impressive on a P&L.
