Architecting Conversational AI Agents for Production

You’ve probably built (or considered building) an AI agent by now. Maybe it answers customer questions, helps users navigate a product, or performs internal tasks like summarising documents or pulling data from systems. Building the prototype is fun, but deploying it — that’s where things get real.

Just like building a Customer Data Platform (CDP) on Google Cloud, developing a conversational AI agent for production use takes more than a clever idea or a well-tuned model. It requires a solid architectural foundation — one that’s scalable, secure, observable, and aligned with your infrastructure and governance policies.

The same principles apply: modular design, strong data practices, and clear guidelines to ensure the system can grow and adapt over time.

In this post, we share key architectural principles to help you transform your AI agent from proof of concept into something that can live and thrive in the real world.

Structured Context Leads to More Reliable Results

A successful conversational AI agent depends on more than model quality. Its performance is heavily influenced by the structure and clarity of the context it receives.

That’s why adopting a data-centric mindset is essential. Instead of focusing solely on prompt engineering and tuning models, this approach emphasises the preparation, organisation, and flow of data.

In a well-designed agent system, data becomes a first-class citizen: carefully curated, consistently structured, and easily accessible. This not only improves how agents consume information but also how they generate and exchange it, leading to systems that are easier to monitor, scale, and trust.

Agents Operate Within Systems, Not in Isolation

Conversational AI agents interact with tools, APIs, and often other agents. This means they need to be part of a system, not just a standalone interface.

Designing AI systems as modular agent ecosystems, where each component or agent is specialised and optimised for a specific task or domain, enables greater performance, scalability, and maintainability.

Architect AI agents to integrate seamlessly across systems by leveraging the Model Context Protocol (MCP) for structured, portable context handling and the Agent-to-Agent (A2A) Interoperability Protocol for secure, dynamic collaboration with other agents.

Modularity also makes it easier to adjust components independently as requirements change or models evolve.

Governance Builds Confidence and Enables Scale

Once AI agents begin to handle sensitive tasks or interact with business-critical systems, governance becomes essential.

This includes defining what agents are allowed to do, how decisions are logged, and when human intervention is required. It also involves monitoring model usage, tracking performance, and applying access controls. Building these elements into the architecture provides the guardrails needed to scale safely and responsibly.

Without this structure, agents may work in isolated use cases, but broader adoption will be difficult to justify or manage.

Security and Privacy Are Architectural Concerns

Security isn’t something to add after deployment. It needs to be part of the design from the start. Conversational AI agents often operate autonomously and interact with data that may be sensitive or regulated. That’s why access control, anonymisation, and auditability are critical. Some implementations benefit from a dedicated “security agent” that approves or inspects high-risk actions before they’re executed.

Data loss prevention (DLP), scoped permissions, and monitoring for unusual behaviour are all part of a responsible deployment strategy.

Iteration Drives Maturity

Conversational AI systems are not deterministic. Their outputs vary based on input phrasing, context, and internal state. That makes predictable behaviour harder to guarantee, and makes iteration essential.

Working in short, focused development cycles helps. This includes regularly testing new prompts, adjusting context formats, and evaluating results using both human feedback and automated scoring. This kind of structured iteration allows the agent to improve over time, without relying on guesswork.

Stability and performance are not the result of one successful prompt; they’re the product of a reliable, repeatable development process.

Observability is a Prerequisite for Reliability

Observability is what enables teams to understand how a conversational agent is behaving in production — and why.

A well-designed system will trace agent behaviour from input ingestion to output generation. It will log decision paths, tool calls, latency metrics, and failure cases. This makes it possible to debug issues quickly, monitor trends over time, and comply with internal and external requirements.

Without this level of visibility, identifying root causes and improving the system becomes a matter of speculation.

Built for Real-World Conditions

A conversational AI agent isn’t useful unless it works in real conditions. That includes the ability to scale, interact with existing systems, respect security boundaries, and respond to operational signals.

Example architecture

This often involves combining familiar cloud architecture patterns — managed services, APIs, load balancing, observability tooling — with newer agent components like LLMs, memory modules, and context builders. A clear separation between orchestration, reasoning, and execution makes it easier to manage complexity.

The result is not just a functional agent, but one that’s maintainable, governed, and production-ready.

Final Thoughts

Building a conversational AI agent is a first step. The real work begins when you turn it into a stable, trusted component within your architecture.

This transition doesn’t require reinventing infrastructure. It requires applying proven architectural principles to a new kind of system — one that reasons, interacts, and evolves. With the right structure in place, conversational AI agents can move beyond prototypes and become long-term contributors to your technology landscape.

Solutions like OLAF — our Online Analytical Friend — demonstrate how applying these principles results in an agent that not only understands data but operates reliably at scale in a real-world environment.