Building AI agents is often discussed in terms of models, prompts, and tools, but in practice, successful agent implementations are shaped far more by foundational decisions around data, governance, and operational maturity. Organizations that skip these fundamentals tend to struggle later with unreliable outputs, security gaps, and systems that do not scale beyond experimentation.
A practical approach to building AI agents follows a clear progression—from preparing the data foundation to choosing the right level of agent complexity and platform maturity.
TL;DR
Here’s the quick breakdown of what you need to know about Building AI Agents at Enterprise Scale
How AI Agents Differ
Not all agents are created equal and treating them as a single category often leads to mismatched expectations and design decisions. Agent complexity depends entirely on the use case, the level of autonomy required, and the risk tolerance of the organization.
Understanding this spectrum helps teams choose the right approach rather than overengineering or underdelivering.
1. Retrieval Agents (Simple)
Retrieval agents represent the simplest form of AI agents, focusing primarily on accessing and returning information.
These agents search documents or structured data, answer basic questions, and are entirely prompt-driven, responding only when a user explicitly asks for input. In practice, they function as a smarter search engine, offering improved relevance and contextual understanding compared to traditional keyword-based search.
2. Task-Based Agents (Moderate)
Task-based agents introduce an additional layer of capability by performing defined actions on behalf of users.
They can send emails, summarize documents, update records, or complete other narrowly scoped tasks, while still remaining user-prompted and limited in autonomy. Although they are more complex than retrieval agents, they remain focused on specific, predictable workflows rather than broad decision-making.
3. Autonomous Agents (Advanced)
Autonomous agents sit at the far end of the spectrum and introduce a fundamentally different operating model.
These agents can continuously monitor systems, take actions without direct prompts, and maintain partial independence from human input. Because they do not rely solely on user interaction to operate, they introduce higher levels of power—and correspondingly higher levels of risk—making governance, monitoring, and control essential.
How to Build Agents that Understand Your Data with Copilot
What Are the Different Ways to Build AI Agents?
Choosing how to build agents is as important as deciding what those agents should do, since each approach offers different levels of control, scalability, and operational complexity.
1. Off-the-Shelf AI Tools
Examples: Copilot, ChatGPT, Claude
These tools are typically the starting point for most teams. They work well when the goal is quick access to information or basic productivity support, with minimal setup.
Benefits
- Fastest time-to-value
- No infrastructure or development effort required
- Useful for quick exploration and basic knowledge retrieval
Limitations
- Minimal control over output quality and format
- Limited or unsafe access to internal enterprise systems
- Weak observability, logging, and auditability
- Poor fit for governed, production-grade use cases
2. Low-Code Platform: Copilot Studio
Copilot Studio is often the next step when teams want more control without moving fully into custom development. It enables faster validation of use cases while staying within a governed Microsoft ecosystem.
Benefits
- Low-code / no-code development for rapid iteration
- Secure access to enterprise data sources, not just static documents
- Native integration with Microsoft 365
- Well suited for MVPs, pilots, and internal-facing agents
Limitations
- Limited control over output consistency and response quality
- Constraints around scalability and modular design
- Multi-step orchestration can become complex
- Limited CI/CD and automated testing support
- Requires careful governance when working with sensitive data
3. MCP Servers and Custom Orchestrators
As agent behavior and workflows become more complex, organizations often move toward MCP servers and custom orchestration. This approach provides deeper control at the cost of higher engineering effort.
Benefits
- Fine-grained control over agent behavior and outputs
- Support for complex, multi-step, and multi-modal workflows
- Stronger access control, logging, and monitoring
- Reusable agent patterns and modular architecture
Limitations
- Higher engineering and maintenance effort
- Requires strong AI and platform architecture expertise
- MCP ecosystem is still evolving
- Increased operational overhead compared to low-code approaches
4. Enterprise-Grade Platforms
Enterprise-grade platforms are built for scale, reliability, and compliance. They are typically used when AI agents are mission-critical or operate in regulated environments.
Benefits
- Designed for production-scale and compliance-heavy environments
- Full MLOps and LLMOps lifecycle support
- Strong governance, approval gates, and environment separation
- Built-in testing, monitoring, and controlled rollouts
Limitations
- Higher upfront investment and platform complexity
- Longer implementation and onboarding timelines
- Requires mature cross-functional teams
- Overkill for early-stage or narrowly scoped use cases
Steps to Build AI Agents at Enterprise Scale
Once the agent type and platform approach are clear, execution must follow a disciplined foundation-first path.
Step 1: Centralize and Govern Your Data
AI is only as good as the data behind it, and this reality becomes more apparent as organizations move from experimentation to real-world deployment.
The first priority is aggregating data from all relevant sources into a single, governed platform where security, access control, and governance policies can be applied consistently. Centralization is not just about convenience; it enables visibility, reduces fragmentation, and ensures that AI systems operate on trusted and authorized information.
Without this foundation, AI adoption becomes risky and fragmented, as agents may rely on incomplete, outdated, or improperly secured data, leading to unreliable behavior and increased operational exposure.
Step 2: Clean and Prepare the Data
Once data is centralized, attention must shift to data quality, because quality directly impacts AI outcomes in ways that cannot be corrected later through models or tooling.
Before building anything, data must be clean, consistent, and of high quality, ensuring that it accurately represents the business context it is meant to support. This preparation step often requires addressing duplicates, inconsistencies, outdated records, and gaps across datasets.
Bad data in inevitably leads to unreliable AI out, and there are no shortcuts here. Even the most advanced models will produce weak or misleading results if the underlying data foundation is not sound.
Step 3: Build Generative AI Applications
With a governed and well-prepared data platform in place, organizations can begin building generative AI applications that deliver real business value.
This is where large language models or small language models are applied, data is tuned for relevance and context, and user experiences are optimized to align with specific operational needs. At this stage, the focus shifts from infrastructure to outcomes—how effectively AI applications support users, decisions, and workflows.
Microsoft Fabric and Copilot Studio are strong starting points for this phase, as they provide native capabilities to connect data, apply models, and begin experimenting with agent-based experiences in a controlled manner.
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How ProArch Helps
Building AI agents is not just a technology challenge—it is a data, governance, and operational problem.
ProArch helps organizations across the entire AI agent journey, from establishing a strong data foundation to designing, building, and scaling enterprise-grade agents. Through our Microsoft 365 Copilot Agents Development and Microsoft 365 Copilot Services, we work closely with teams to ensure agents are aligned with real business workflows and enterprise controls.
Our teams help clients:
- Assess agent use cases and select the right level of autonomy
- Design secure, governed data platforms for AI readiness through Microsoft Fabric Consulting & Implementation
- Build and operationalize agents using Microsoft Fabric, Copilot Studio, and custom orchestration approaches
- Implement testing, monitoring, and governance to support responsible and scalable AI
By combining deep Microsoft ecosystem expertise with practical engineering and security experience, ProArch enables organizations to move beyond experimentation and build AI agents that are trusted, scalable, and ready for real-world enterprise use.
