Everyone can build an AI agent today.
The hard part isn't getting an agent to answer a question or complete a demo. It's deploying one that employees trust, security teams approve, and operations teams can manage at scale.
That's where many AI projects stall.
As organizations move beyond experimentation, the conversation shifts from prompt engineering to production readiness. Can the agent safely access business data? Can you evaluate changes before deployment? Can you understand why it made a decision? Can you monitor performance over time and improve it with real-world feedback?
These are the capabilities that separate a flashy demo from a useful enterprise-grade agent.
Appian's latest AI capabilities deliver that reality. Rather than treating agents as standalone chatbots, Appian embeds them into the same workflows, governance model, and development lifecycle that already power enterprise applications.
In this article, we'll look at three capabilities every enterprise agent needs and how Appian helps organizations build them
Build agents with existing enterprise assets by reusing your business logic, data, and workflows instead of starting from scratch
Evaluate agents before they reach production with repeatable tests and continuous feedback to improve quality over time
Govern agents with enterprise controls using build-in security, human oversight, lifecycle management, and observability
1. Build enterprise-grade AI agents with your existing enterprise assets
One of the biggest misconceptions about AI agents is that better prompts create better agents.
Prompts matter, but they're only one piece of the equation.
In practice, an agent is only as capable as the business knowledge, tools, and data it can access. The goal isn't to create the perfect prompt—it's to capture how your organization's experts actually perform their work.
What information do they gather before making a decision? Which systems do they consult? What policies do they follow? What actions can they take? Those are the assets that make an agent useful.
Turn existing business logic into agent capabilities
Many organizations already have years of business logic embedded inside their applications. Workflows define how processes run, business rules capture policies and decisions, and data models provide the structure and context for enterprise information.
The challenge isn't rebuilding that business logic for AI—it's making it available to agents.
Appian agents use many of the same application assets developers already use:
Process models
Expression rules
Data fabric
Documents
External services through MCP
Instead of creating another integration layer, existing application logic becomes reusable agent tools.
This also solves one of the biggest barriers to enterprise adoption: the cold-start problem. Organizations don't need to build an entirely new ecosystem of agent-specific capabilities before seeing value. Agents can immediately leverage the workflows, business logic, and enterprise data.
Build tools around business tasks, not technical APIs
Giving an agent access to an API doesn't automatically make it effective.
Traditional APIs are designed for human developers who know exactly which function to call and in what sequence. Agents operate differently. They receive broad business goals and must determine the best way to accomplish them.
The more orchestration the model has to perform, the more opportunities it has to make mistakes.
That's why the best agent tools are designed around business tasks rather than low-level operations.
Take a customer order as an example. Instead of exposing separate APIs to retrieve a customer, check inventory, create an order, and send a confirmation, expose a single tool like Process Customer Order. The tool handles the underlying workflow so the agent can focus on fulfilling the user’s request.
Appian delivers this experience by allowing agents to invoke existing business objects that already contain the business logic and validations. Rather than asking the model to coordinate multiple technical operations, developers expose higher-level business actions that agents can use reliably.
Connect agents to the broader AI ecosystem
No organization runs entirely on one platform. Business data lives across internal applications, SaaS products, and third-party services. Enterprise agents need to work across that ecosystem.
Appian supports bidirectional MCP interoperability. Agents can invoke external MCP tools to interact with systems outside Appian, while external AI applications can access Appian tools and resources through the Appian MCP Server.
This lets organizations extend agent capabilities while continuing to reuse existing business logic they already trust, instead of rebuilding it for every AI framework.
2. Evaluate AI agents before they reach production
Building an agent is only half the job. The other half is proving that it works before you deploy it.
Traditional software testing focuses on deterministic behavior: given the same input, the system should always produce the same output. AI agents are different. They can produce different but equally valid responses to the same request, making evaluation fundamentally different from traditional QA.
That makes evaluation one of the most important disciplines in enterprise AI.
Appian brings evaluation directly into the agent development lifecycle. Instead of relying on ad hoc prompt testing or manual spot checks, developers can define test cases, execute bulk evaluations against a specific agent version, and review the results in one place. Evaluation becomes part of the same workflow used to design and refine agents, making it practical for testing every change before deployment.
Define success before you build
One of the most common mistakes teams make is treating evaluation as the final phase of development. In practice, it should begin much earlier.
When you define what an agent should do, you should also define how you'll measure its success.
For example, for a customer service triage agent, evaluation criteria might include:
Was the request routed to the correct team?
Were all required data points collected?
Was escalation triggered when appropriate?
Did the response meet the expected service level?
These criteria are much more useful than asking whether the response "looked good." They establish objective expectations that can be measured consistently over time.
Appian's evaluation capabilities are designed around this approach. Developers define representative test cases alongside the agent itself, creating a repeatable benchmark for future iterations.
Build an evaluation suite, not just a handful of examples
A few successful prompts don't tell you whether an agent is ready for production. A strong evaluation suite should reflect the situations the agent will encounter in the real world, including:
Common business scenarios
Edge cases
Ambiguous requests
Missing or incomplete information
Tool failures
Conflicting instructions
Running the same evaluation suite after every meaningful change provides a consistent way to measure whether the agent is improving or whether a prompt or model update introduced regressions.
Because Appian evaluates a specific version of an agent, teams can compare results across iterations before deciding what to promote to production.
Use production feedback to improve performance
Evaluation doesn't stop after deployment. Some of the most valuable feedback comes from people already doing the work. Every approval, correction, override, or rejection reveals something about how the agent is performing in real business situations.
The organizations seeing the fastest progress with AI agents treat this as a continuous improvement loop:
- Define success criteria
- Evaluate against representative test cases and identify failure patterns
- Improve the agent's instructions, tools, or knowledge
- Reevaluate and repeat
Because Appian agents operate inside business workflows, organizations can capture production feedback where work already happens. Managers approve recommendations, analysts correct classifications, and employees refine agent outputs as part of their normal processes.
Over time, both the evaluation suite and the production feedback become organizational assets. They help teams confidently adopt new models, refine prompts, and expand agent capabilities without sacrificing quality.
3. Govern AI agents with enterprise controls
As agents move from prototype to production, organizations must ensure they can trust them to operate safely at scale. Enterprise agents need governance built into the platform—from access controls and human approvals to version management and operational visibility. These capabilities allow organizations to scale AI adoption without sacrificing security, compliance, or control.
Use the same security model that already protects your business
An AI agent should operate within existing security policies.
Rather than introducing a separate permission model for AI, Appian extends the same governance framework that already protects enterprise applications. When an agent accesses data fabric, invokes a process model, or executes an expression rule, it inherits the same object permissions and record-level security as the user initiating the request.
This approach has two advantages. First, organizations don't need to duplicate or maintain a separate set of AI-specific access policies. Second, agents automatically evolve alongside existing security controls, ensuring governance remains consistent regardless of whether work is performed by a person, a workflow, or an AI agent.
Enforce policies through the platform, not the prompt
Prompts can tell an agent what it should do. Enterprise platforms need to enforce what it can do.
For example, if a payment above a certain threshold requires approval, that rule shouldn't exist only as an instruction in the prompt. It should be enforced by the workflow. The same principle applies to sensitive customer data. Access should be controlled by platform permissions, not by trusting the model to follow instructions. Appian combines multiple layers of governance to make this possible. Existing process controls define how work flows through the organization, while AI guardrails provide centralized policies that inspect agent inputs, tool interactions, and outputs to help detect sensitive information or policy violations before work continues.
The result is a governance model where policies are enforced consistently instead of relying solely on the model to follow instructions.
Design human oversight into the workflow
Another common misconception about enterprise AI is that success means removing people from the process.
In reality, the most effective systems combine AI with human judgment.
Some decisions can be automated end to end. Others require review because they involve financial risk, regulatory compliance, or customer impact. The challenge isn't maximizing autonomy; it's deciding where autonomy is appropriate.
Because Appian agents execute within process models, adding human review doesn't require a separate orchestration layer. An agent can gather information, analyze documents, prepare a recommendation, or draft responses before handing the work to a person for approval.
This allows organizations to gradually expand agent autonomy while maintaining appropriate oversight for high-impact decisions.
Manage and monitor agents in production
Building an agent is only the beginning. Enterprise teams also need to manage change over time.
As prompts evolve, tools are added, and foundation models improve, organizations need a reliable way to develop, test, and promote updates through existing software delivery processes.
Appian treats agents as first-class design objects rather than standalone configurations. They support versioning, lifecycle management, import and export, and deployment through the same DevOps practices used for the rest of the application.
Organizations also need visibility into how agents perform in production. Which tools are being called? Where do failures occur? When are humans overriding recommendations? Which workflows consume the most AI resources?
Appian provides visibility into individual agent runs, including execution history, tool calls, inputs, outputs, and reasoning flow. Combined with AI usage metrics and workflow monitoring, this visibility helps teams understand both agent effectiveness and operational cost.
This visibility helps organizations improve prompts, simplify tool design, optimize workflows, and identify opportunities to replace repetitive agent decisions with deterministic automation where appropriate.
Enterprise AI is about operational excellence, not just model intelligence
Foundation models will continue to improve. New frameworks will emerge and best practices will evolve.
What will matter most is an organization’s ability to successfully deploy AI in production at scale.
That's the philosophy behind Appian's approach to AI. Rather than treating agents as standalone assistants, Appian makes them part of the enterprise operating model. Agents reuse existing business logic, operate within governed workflows, inherit the same security policies as human users, and move through the same development lifecycle as the rest of your applications. As organizations adopt more capable models or entirely new ones, those durable operational foundations remain in place.
Building an agent is becoming easier every day. Building one that your organization can trust, govern, and continuously improve is the real challenge, and that’s where the Appian Platform makes the difference.
Explore Appian's AI capabilities and see how to build, govern, and scale AI agents with confidence.
