Why AI Agents Are Hard to Build, and How MCP Makes It Easier

AI agents can automate critical workflows. However, these agents encounter challenges that hinder real action and reliability. Let’s break down why building and deploying AI agents is challenging and why a new approach called Model Context Protocol (MCP) offers a compelling solution.

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Why Building AI Agents Is So Difficult

AI models have a short memory. Agents lose context between steps of a task, especially in extended conversations. A multi-step agentic workflow, involving fetching data from a database, analyzing it, and drafting a report, needs the model to retain memory and context for execution.

If there’s a task on fetching data from a database, analyzing it, and drafting a report, AI agents don’t inherently know how to plan and execute such workflows reliably.
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Developers often must hand-craft complex logic to break tasks into steps and feed the right context into the AI model at each step. Any change or error along the way can derail the whole process.

Coordinating these multi-step processes (often called “agent orchestration”) is complex and challenging to get right. The agent needs to reason, make intermediate decisions, and adjust if something changes, abilities that go beyond simple question-answering.

Moreover, for an agentic system to actually do something useful, it often needs to hook into external systems. If it is querying a database or calling an API, each integration is traditionally a one-off project.

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Why Enterprises Have an Even Harder Time

Using the challenges, let’s paint a picture of what an onboarding agent will have to do to onboard a loan applicant.

The agent will have to deal with a fragmented tool landscape

The agent will need multiple tools to complete the onboarding process. These tools span from signature detection to liveness detection and bank statement analysis to information retrieval from enterprise databases. Each of these is a separate integration—different APIs, auth schemes, data formats—so building and maintaining all the connectors quickly becomes a significant engineering effort.

The agent has to manage data security and governance

Every request (e.g., raw IDs, financial docs) must traverse TLS-encrypted channels. Role-based checks ensure the agent only sees PII it’s permitted to, and every action is logged immutably for compliance (e.g., RBI, GDPR).

If the OCR service ever needs to send data to a cloud endpoint, it must first pass through an enterprise data firewall—otherwise, the workflow halts.

The agent has to traverse the complex infrastructure

A bank’s loan-processing engine and other workflows still run on legacy systems. Some services reside in private data centres, while others are hosted in AWS or Azure, and a few are SaaS offerings.

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What Can MCP (Model Context Protocol) Do?

MCP standardizes how AI agents connect to data and tools. If the challenges above sound complex, MCP’s core idea is refreshingly straightforward: provide AI agents with a consistent, universal way to integrate into the systems and data they need, regardless of where those systems reside or who built them.

Instead of each integration being a bespoke one-off, any tool or database can expose an MCP-compatible interface (by running a small MCP “server” or adapter for that tool). And any AI application or agent can include an MCP “client” component that knows how to talk to those adapters. Once both sides speak MCP, the AI agent can request information or actions in a standard format, and the tool’s adapter will execute it and return results in a structured, standardized response.

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Here’s a video of Weave doing exactly that:

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How MCP Simplifies and Strengthens AI Agent Development

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By introducing a universal interface for AI tools interaction, MCP directly tackles many of the challenges we discussed earlier.

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1- Standardized Integration

MCP replaces one-off hacks with a unified protocol. The result is a more reliable system with fewer moving parts.‍

2- Maintained Context and Workflow State

One of MCP’s powerful features is that it provides a structured way for an AI to retain and update context across a workflow. ‍

3- Flexibility to Switch and Scale

MCP is model- and platform-agnostic. You could use any LLM or SLM. As long as they support MCP, your integrations still work. Multi-agent orchestration is a different ball game.  

‍4- Structured Error Handling and Reliability

Because MCP enforces a structured request/response format, it inherently encourages better error handling and feedback. MCP servers are designed to catch errors and return meaningful messages that the AI can understand.

‍5- Ecosystem and Support

MCP is not a fringe idea; it’s gaining rapid adoption. It’s open-source and supported by major AI players. Anthropic open-sourced the protocol in late 2024, and since then, companies such as Block formerly (Square) and Apollo have integrated MCP into their systems.

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AryaAI’s Weave: Agent Orchestration Platform Built for Enterprises

To make MCP more than just a protocol, AryaAI built Weave, a turnkey platform that embodies every promise of Model Context Protocol for real-world enterprise AI agents.

Weave turns the complexity of integration and orchestration into a low-code, secure, and scalable experience. You can connect Arya.ai’s APEX (pre-trained AI models) with over 100 external applications.

With Weave, you eliminate bespoke integration code and governance headaches—so your AI agents become reliable, scalable, and enterprise-ready in days.

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Conclusion

Building AI agents that truly work in a complex enterprise setting has been historically challenging. The difficulties of memory management, workflow orchestration, integration, and security have arrested the growth of AI agents in enterprises. MCP frees your team from reinventing the wheel for each tool or dataset.

For business leaders, the value of MCP is in its high-level simplicity and trustworthiness. It can cut down development time by providing a “plug” for new capabilities. The bottom line: developing AI agents is challenging, but innovations like MC are making it significantly easier.