By Sugandha Srikanteswaran, Head of Infrastructure and Platforms, The Hartford India
Software Engineers know the difference between code that works once and code that runs reliably. Demonstrating a proof of concept focusses on getting a functional or a capability ask in a proven action. A feature passes in development, behaves perfectly in a controlled test, and looks completely convincing in a review. Then it hits a real environment. Users engagement patterns, dependencies shift, and the system exposes invisible assumptions.
AI agents are hitting this exact reality right now. A demo works perfectly because the path is clear or defined, the prompt is carefully written, and the tools behave. ,Production brings out the unknown. Organisations must build systems that last.
What happens to Demo-Ready Agents over time?
Building an agent usually starts with one massive prompt. You stuff everything in there: the task, the tone, business rules, exceptions, and formatting. This gets the experiment off the ground quickly. But this structure breaks down as the workload grows. The prompt gets dense with patches for edge cases and scattered tool instructions. Soon, the entire system balances on a fragile mix of memory and unstated assumptions.
When the agent fails, diagnosing the issue is nearly impossible. It might deliver a polished answer built on a weak tool call or an outdated assumption. Treating prompt edits as quick fixes builds technical debt instantly.
Prompts Are System Logic
The conceptual shift between coding and prompting is massive. Prompts act as system behavior, dictating how an agent interprets goals, uses tools, handles uncertainty, and escalates issues or in other words defining the intent of work. Because prompts change production workflows, they require strict software discipline.
Therefore, teams must version, review, test, document, and own these instructions. Every change needs a clear reason and an understood risk because managing prompts informally leads to chaos. A single line is added to correct one poor response. Another instruction is rewritten to meet a stakeholder’s preference. Over time, the prompt becomes harder to read, harder to test, and harder to control. Treating prompts as operational assets prevents this reliability debt.
Building for Maintainability
Maintainable agents rely on architecture, leaving the oversized mega-prompt behind – Remember Architecting or describing the Intent. They use smaller, testable components with explicit jobs. One component classifies intent, another decides on tool use, and a third formats the final response. This separation lets teams inspect behavior and improve individual layers safely.
Vague instructions fail in production, so agents need defined contracts, clear input expectations, validated outputs, and defined failure handling. If a tool returns missing data, the agent needs a clear path forward and if an API returns garbled text, the system must catch the error early. Maintainability is a core design principle.
Testing the Messy Reality
Agents perform perfectly on their training examples, creating false confidence. Real enterprise data is messy, full of missing fields, duplicate records, and conflicting information. Maintainable agents require strict evaluation gates and regression tests. Teams must test every prompt update against known edge cases and past failures before it goes live.
Rollback paths are equally crucial. A prompt change might make an agent helpful in routine cases but reckless in high-risk scenarios. Having a quick way to restore previous behavior saves teams during an incident. Every update must answer a simple question: what behavior improves here, and what risks accompany it?
Mapping the Agent’s Path
Traditional software observability usually tracks uptime, latency, error rates and system health. Agentic systems need a deeper layer of visibility because the final response alone does not reveal how the answer was produced.
Teams need to see the agent’s reasoning path with clarity: which prompt component shaped the decision, which tool was called, what inputs were passed, what outputs were received, and whether those outputs were validated before the next step. This matters because an agent can produce a confident, well-structured response even when the logic behind it is flawed.
When something goes wrong, incident playbooks should help teams trace the failure to its source. Was the prompt unclear? Did the tool return weak data? Was the output validation skipped? This level of visibility moves teams away from reactive prompt tweaking and towards disciplined operations.
Setting Clear Automation Boundaries
Capable agents need strict limits. In low-risk workflows, we can allow the agent to complete the task independently. In sensitive workflows, we need it to draft, recommend, flag, or escalate rather than act on its own.
This boundary must be designed upfront. A production-ready agent should clearly define where automation is acceptable and where human judgement is required. It may summarize a case, while the final approval stays with a human. It may identify a payment error, while the actual routing moves through a verified manager.
This is the value of bounded autonomy. It gives organisations the speed of agentic systems without exposing critical decisions to unchecked automation. As agentic AI becomes part of enterprise infrastructure, reliability will depend on clear contracts, tested components, monitored reasoning paths, rollback options, and operational ownership. That is how we turn a working idea into a dependable system.