What You Can’t Script: The Real Requirements for Building AI Agents

You’re hearing a lot about AI agents—but what does it actually take to build one? If you’re exploring AI agent development, you’re probably asking the same questions as every forward-thinking leader: Where do you start? Should you build in-house or buy from a specialized vendor? 

How do you make sure your agent isn’t just a chatbot but a system that can reason, act, and deliver measurable ROI? In this post, we’ll unpack the core building blocks of AI agent development, the pitfalls that derail most pilots, and what you need to know to get it right the first time.

Adoption is accelerating: Nearly 80% of senior executives now say they use AI agents, and two-thirds of them have already seen measurable productivity gains.

AI agents represent a clear opportunity to scale support, accelerate work, and free employees to focus on higher-value tasks — but realizing that potential requires building them the right way and with the right tools. Here’s how to do it.

What separates an AI agent from standard automation approaches

You’re probably already using standard automation tools like RPA or iPaaS to keep workflows running smoothly. These are great for repetitive, rules-based tasks, but once your processes get more complicated, unpredictable, or stretch across multiple systems, the tools can struggle.

AI agents shine in scenarios where traditional automation tools fall short. 

That’s because AI agents are a new class of AI powered by large language models and enterprise integrations. AI agents can understand goals, reason through problems, adapt in real time, and take action across enterprise systems without step-by-step instructions. 

• Unstructured and dynamic tasks: Unlike RPA (which falters if a UI changes) or iPaaS (which needs fixed rules), AI agents can adapt to new data and interfaces. So an agent can troubleshoot a software glitch even if the error message is new.

For example, if an employee requests help with a slow laptop, the agent can walk through diagnostics, review system logs, and suggest cleanup steps — even if the exact error hasn’t been seen before

• Multi-step, cross-system processes: Multi-agent setups can manage orchestration across apps, like handling an IT incident by diagnosing the issue, notifying the right teams in Slack, and applying a fix, all without a script.
• Achieving high-level, goal-oriented outcomes:  Instead of following rigid steps, agents work toward objectives once given a task. 

For example, if a manager requests onboarding for a new hire, the agent can provision accounts in Okta, request a laptop through the asset system, and schedule orientation sessions in the HR platform.

True AI agents stand out because they: 

• Rely on natural language understanding (NLU), so you can just say “I can’t log in” or “Fix my account” instead of using a specific keyword or system-specific command.
• Adapt to new scenarios via reasoning and memory, handling edge cases without breaking.
• Actually take action across systems, like updating a ticket in your ITSM rather than handing it off to a human.
• Operate in real-time across multiple tools, not just one channel.

In short, RPA and iPaaS automate fixed processes, while AI agents resolve, make decisions, and help resolve complex work in real time.

Set your organization up for success with the ultimate guide to deploying AI agents.

6 core components of a true AI agent

If you want an AI agent that can take on complex, real-world work, it needs the right foundation — miss one, and your agent might run into roadblocks, frustrate users, or even create risks you didn’t expect.

Here are key elements of AI agents at a glance:

• Natural language understanding (NLU): Interprets real human language with nuance and accuracy.
• Reasoning and decision-making: Breaks goals into steps, adapts, and chooses the best path forward.
• Memory and context persistence: Remembers past interactions and keeps context across conversations.
• Tool access and enterprise integrations: Connects securely to the existing systems needed to act, not just talk.
• Governance, compliance, and escalation handling: Operates within rules and knows when to pause or escalate.
• Learning, monitoring, and optimization loops: Improves continuously through feedback and oversight.

Let’s break down each component to see exactly how it works and why skipping even one will limit your agent’s effectiveness.

1. Natural language understanding (NLU)

NLU is the brainpower that lets an AI agent understand what you mean, no matter how you say it. Unlike basic bots that rely on exact commands, NLU processes natural language, picks up on intent, extracts key details, and handles even multi-part queries. 

For example, whether you say “I can’t log in again” or “I forgot my credentials,” an NLU-powered agent knows you’re asking for a password reset and takes the appropriate actions. These capabilities are typically powered by large language models (LLMs) combined with domain-specific training and enterprise data grounding, rather than depending on specific vendors.

Without NLU, your agent could easily misunderstand a request and leave users stuck or frustrated. Imagine an employee asking, “Why’s my laptop so slow?” Instead of just saying, “Contact IT,” an NLU-powered agent can iterate through possible causes, surface diagnostic info, and suggest a fix right away.

2. Reasoning and decision-making

A smart AI agent doesn’t just follow a rigid checklist — it thinks through the situation before taking action. Take something like granting access to a new software tool. It’s rarely a simple yes or no. 

A reasoning-capable agent can:

• Check the user’s department.
• Verify their location or remote status.
• Review past access requests.
• Decide if approval should be automatic or escalated.

Without this capability, you’d need to hardcode dozens of rigid decision paths for every possible scenario. But with it, the agent can make decisions dynamically — while still following enterprise policies and guardrails to stay compliant. 

Today’s agentic AI is advancing quickly, but it still requires careful setup, rules, and integrations — it isn’t “plug and play.”

3. Memory and context persistence

Memory is what lets an AI agent maintain continuity across interactions. Say you asked for a new laptop three days ago and want to get a shipping update. Instead of having to type in the order number or manually hunt down tracking info, you can just ask the AI agent, “Where’s my new laptop?” 

The agent can remember the previous interaction, understand the context, and follow up with shipping updates, saving you time and frustration. Without memory, users have to repeat themselves, and the agent ends up seeming “dumb” or disconnected.

Memory comes in two main forms:

• Session (short-term) memory: Holds context for the current conversation or task. Once the session ends, it’s gone.
• Persistent (long-term) memory: Stores information for reuse across sessions, enabling personalization and learning over time. 

Persistent memory also raises privacy and compliance considerations (e.g., retention limits, data access rules), which must be handled with governance in mind.

• Retrieval-based memory: Uses knowledge bases or vector databases to recall relevant data without storing sensitive details indefinitely.

Both improve agent performance, especially in prototyping and development process stages, where feedback is frequent.

4. Tool access and enterprise integrations

An AI agent’s capabilities depend on the environment it operates in. For many enterprise use cases, going beyond answering questions and actually taking meaningful action (like updating support tickets, granting access, or pulling employee info) requires secure, reliable connections to your key enterprise tools.

In these cases, integrations aren’t optional — they’re what allow AI agents to act across IT, HR, finance, and customer platforms, not just provide information.

Some of the essential systems it might connect to include:

• IT service management (ITSM) platforms like ServiceNow or Jira Service Management, where the agent can create, update, or close support tickets automatically.
• Human resource information systems (HRIS) such as Workday or SAP SuccessFactors let the agent retrieve employee details, process leave requests, and help with onboarding tasks.
• Identity and access management (IAM) tools like Okta or Microsoft Azure Active Directory allow the agent to securely grant or revoke user access based on company policies.
• ERP platforms like SAP or Oracle for finance and operations
• CRM tools like Salesforce to manage customer workflows

The agent must integrate with these back-end systems through well-designed, secure connections. This often means using APIs, tokens, SDK connections, service accounts, or role-based access control (RBAC) that ensure the agent has the right permissions while keeping your data safe.

Integrations should also include auditability, monitoring, and rate-limiting to ensure security and performance.

Without this kind of access, your AI agent is limited to sharing static information or handing off requests to humans because it can’t take real, autonomous action. 

5. Governance, compliance, and escalation handling

Since AI agents work with a lot of independence, they need clear rules about what to do and when to pause and ask for help. Think of this as setting up guardrails that keep your agent on the right track.

For example, if someone requests access to sensitive financial data, your agent should flag the request and route to your IT security team for approval — not act on its own.

Good governance also requires compliance measures such as audit logs, data handling rules, and adherence to privacy standards. And when agents lack enough context, they should escalate to a human or rather than guessing or stalling.

This careful balance of autonomy and oversight helps the agent operate confidently within your company’s standards, reducing risks and building user trust. 

6. Learning, monitoring, and optimization loops

Advanced AI agents aren’t static. They should improve over time through user feedback, admin updates, or error logs.

If a user isn’t satisfied with how the agent handled a support request, that feedback can be used to refine workflows, adjust prompts, improve integrations, or flag the issue for a human to review and guide improvements. This doesn’t mean the agent retrains its own models — retraining is typically handled periodically at the platform level.

These learning loops, like error logging and human-in-the-loop oversight, work best when paired with monitoring and observability. Dashboards, error rates, and performance metrics help teams catch issues early, while governance ensures changes remain auditable and compliant.

Done well, these loops turn agents into evolving systems that stay sharp, relevant, and reliable — rather than static bots that repeat the same mistakes.

What makes AI agent development challenging

Building AI agents is far from a plug-and-play process. If you’re not careful, you can run into roadblocks that slow progress or lead to costly missteps. Common challenges include:

• Identifying high-impact workflows: With so many potential use cases, the real challenge is pinpointing the workflows that offer meaningful impact. Not just the low-hanging fruit or flashy ideas, but those that truly boost efficiency, save time, or improve user experience. Finding these “sweet spots” takes a strategic approach and careful prioritization.

• Understanding employee and business requirements: Translating what your users and business units really need into AI capabilities is often the toughest part. Each organization is unique, and needs can be complex or constantly changing. Without a deep understanding, agents risk missing the mark or causing frustration.

• Reliability and trust: AI agents must perform consistently, even in edge cases. When they do, productivity can soar. But if they hallucinate, fail to execute tasks, or break during integrations, they can disrupt business operations and erode trust. Reliability requires strong testing, monitoring, and fallback mechanisms.

• Integration, security, and usability: Seamlessly connecting AI agents with your existing systems can get complicated fast, especially if you aren’t using a platform that’s really built for enterprise needs. 

Teams must enforce compliance (role-based access control, identity and access management, audit logs, data residency) while also embedding agents in tools employees already use (Slack, Microsoft Teams, etc.) to drive adoption.

• Data quality and availability: Agents are only as good as the data they can access and retrieve. Without high-quality, well-structured, and accessible data, they struggle to reason and act accurately. This often requires significant effort to clean, organize, and integrate data sources.

• Content and ecosystem gaps: Sometimes, the right plugins, integrations, or domain-specific knowledge simply don’t exist for your systems or workflows. This means extra development work to build or customize what your agents need to function effectively.

• Governance and compliance: Because agents act autonomously, they need clear guardrails for escalation, approvals, and auditability. Without this, adoption can stall over trust and risk concerns.

Understanding these challenges upfront can help you plan smarter, avoid common mistakes that derail ROI, and create AI agents that truly deliver.

AI agent development strategy

Building a successful AI agent starts with a solid plan:

1. Decide your development path: Before you choose a platform, you need to define how you want to build your AI agents.
   • From scratch: Maximum flexibility and control, but requires significant engineering resources, longer build cycles, and deep expertise.
   • Low-code/no-code agent builder: Offers prebuilt components, templates, and connectors so you can move faster while still customizing agents for your business.
   • Pre-built agents for specific domains: Fastest path to deployment. Ideal if your goal is quick time-to-value in areas like IT, HR, or customer support, though customization is limited.
2. Select the right platform: Once you know your development path, look for an enterprise-grade platform in that category. Prioritize platforms with
   • Strong documentation and community support
   • Secure, well-documented connectors and APIs
   • Built-in monitoring, governance, and compliance
   • Seamless integration with your existing systems
   • The right platform choice depends on your priorities: control vs. speed, flexibility vs. simplicity, custom vs. prebuilt.
3. Define use cases and requirements: Identify the high-value business problems or workflows your AI agent should address. Focus on areas where automation delivers measurable efficiency, not just flashy experiments. Examples: employee onboarding, IT incident resolution, access provisioning, or benefits inquiries.
4. Understand agent patterns: In practice, most enterprises will use a combination of AI agent types to support these use cases:
   • Aggregation agents collect and summarize information across systems.
   • Action agents execute tasks and transactions on behalf of employees.
   • Ambient agents monitor systems in the background and surface insights proactively.
   • The key is not to pick just one type, but to align the right mix of patterns with the workflows that matter most.
5. Build, test, and validate: Connect your agent to the necessary data sources and enterprise systems. Test it thoroughly across edge cases, escalation paths, and compliance checks. Reliability and trust depend on strong validation before deployment.
6. Deploy with monitoring and governance: Launch your agent with monitoring dashboards, error reporting, and user feedback loops. Establish clear escalation rules and compliance guardrails to balance autonomy with oversight.
7. Continuously improve: Collect feedback, review performance data, and refine workflows regularly. AI agents aren’t static — their value grows as you optimize them for new scenarios, integrate more systems, and close capability gaps.

Build and deploy automations faster with enterprise-ready AI agents

Building enterprise-ready AI agents takes more than just setting up simple automations or scripts. When you develop or integrate AI agents, your role shifts from writing code to architecting an autonomous system that can think, adapt, and act across third-party systems. 

Moveworks is a proven, enterprise-ready platform designed to deliver AI agents that can meet your organization’s toughest challenges from the start. Our platform offers:

• Prebuilt, secure integrations with hundreds of business systems like Salesforce, Workday, ServiceNow, and more so your agents have the tools they need to act, not just inform.
• Fast, low-code development with Agent Studio, enabling your team to build powerful automations quickly, using just a fraction of the code traditional approaches require.
• Intelligent reasoning that helps agents adapt to new requests, handle edge cases faster
• A robust AI Agent Marketplace where developers can instantly discover, validate, and deploy hundreds of agents, accelerating adoption across teams.

With Moveworks, you can reduce the complexity of assembling and maintaining countless components yourself. Instead, you gain a unified platform that supports rapid development, strong governance, and measurable business impact.

Ready to transform your AI agent ambitions into enterprise-wide reality? Explore Moveworks AI Agent Studio.

Want to build your own AI agents hands-on? Register for our upcoming hackathon.