Context, constraints and control: The keys to building reliable purpose-driven AI agents

There's a common assumption that fully autonomous AI can handle any task, any domain, with little to no human oversight. It's an enticing idea, but in practice, it's one of the biggest reasons enterprise adoption continues to stall. 

In truth, while large language models (LLMs) offer impressive capabilities, they also come with a major drawback: unreliability. One prompt might generate something useful, the next, complete fiction. These so-called "hallucinations" aren't rare with a Stanford study finding hallucination rates as high as 88% in legal scenarios. 

This means that if businesses want to build useful AI, they need to work with the grain of the technology. This entails a more grounded approach that embraces constraints: closed-world problems, purpose-built agents, scoped tools, robust evaluation and layered governance. Here are five key considerations when building reliable AI agents. 

Give AI guardrails, not root access

Organisations don't hand sensitive terminal access to every employee. Staff aren't given raw SQL access to production data; instead, they use secure dashboards and custom access tailored to specific tasks. The same principle should apply to AI, which operates best behind clean, structured interfaces.

This means moving beyond the idea of a single chat box as the primary interface. The real opportunity lies in AI systems that are always on, quietly working behind the scenes. They're trained to listen to signals, like a new support ticket, an abandoned cart or an incident alert, and react with purpose within defined boundaries.

But shifting from human-issued prompts to signal-based automation demands a rethink of UX and governance. The idea isn't to build a generic chatbot - it's to design a tightly scoped agent with clear duties, wrapped in compliance layers. 

Focus on closed‑world scenarios

It's also essential to choose the right type of problem for AI to solve. That means concentrating on closed-world scenarios with clear parameters, trustworthy data and measurable outcomes. This can range from processing an insurance claim, to troubleshooting an IT ticket, or onboarding a new customer or employee.

In these cases, the inputs required are known, the outputs are expected, and it's easy to measure success, making it possible to effectively test, audit, and trust LLM-based systems. Take code generation, for instance. It has become one of the standout LLM use cases precisely because you can run the output and verify it. This clarity - well-specified inputs, testable outputs, and instant feedback - is exactly what makes closed‑world applications robust and viable.

Focusing on closed‑world problems allows you to write targeted test cases to define successful outputs, trace decisions easily, and enforce strict data and behavioural guardrails, making the AI system far more dependable.

Build smarter by thinking smaller

When it comes to building effective, scalable AI, it can be helpful to think of it like software: modular, composable and scoped to specific jobs.That's where purpose-built agents come in.

A purpose-built agent is a system that focuses on a single responsibility, like triaging support tickets, analysing system logs or generating weekly sales reports. Each agent is optimised to do one thing effectively. 

And like software, the true power lies in composition - how multiple agents work together. Take processing an insurance claim: it's not a single step but a workflow involving input validation, eligibility checking, relevant policy retrieval, case summarisation, and escalating exceptions. Instead of designing one giant agent to handle it all, build smaller agents, each addressing one component and then managed into a cohesive multi-agent system. This sort of system mirrors the benefits seen in microservices architectures, which enhance reliability through collaboration. 

Give LLMs the tools they need 

After breaking down the AI system into purpose-driven agents, the next essential step is providing each agent with the right tools tailored for machine use. 

LLMs depend entirely on what they're exposed to and how, so designing predictable agents relies on tools designed specifically with that in mind.  A generic tool - say, unrestricted SQL access to your production database - might seem powerful, but it's difficult for an LLM to use correctly. More effective are narrow, self-describing tools that are access-controlled and only usable by authorised agents. Open standards like the Model Context Protocol (MCP) help with this, allowing you to define tools in a structured way so agents can reliably discover and use them. 

And by wrapping LLMs with tightly scoped, well-designed tools, you force reliability. Rather than improvising, the model behaves like software should: following rules, defined behaviour, and delivering predictable results on purpose.

Test the path, not just the output

Testing AI agents goes beyond checking if outputs are correct. It also means verifying how the agent finds tools, decides when to use them, and whether it executes those actions properly. That calls for real-time evaluations, not just eyeballing results.

If the agents and tools are purpose-built and scoped, setting up evaluations becomes straightforward: you define known inputs, expected outputs, and test edge cases and typical flows consistently. These tests need to be deterministic and repeatable, just like any production-grade system.

For many applications, human oversight is vital. Decisions requiring nuance or legal judgment should be routed to humans. This ensures routine tasks are automated, while exceptions are escalated appropriately. 

By combining structured tests with human-in-the-loop checkpoints and robust oversight, you build AI agents that are trustworthy and reliable.

Control enables scale

LLMs are powerful, but power without structure leads to risk. The key to scalable, reliable AI isn't open-ended freedom; it's well-designed boundaries. 

By scoping agents tightly, and wrapping them in the right tools, structures and governance, businesses can develop AI agents that are dependable and deliver real, meaningful value.