'Coding Is Solved'. Operating Software Isn’t.

“Coding is solved” sounds like a productivity breakthrough—until the definition of coding expands to include everything that happens after the code is generated.

The statement may be reasonable if it means that AI can produce working code from a clear request. But software delivery is not only code generation. It is deciding what to build, fitting that change into a living architecture, operating it under real conditions, and learning from what happens next.

That distinction is surfacing in practitioner conversations across DevOps: coding was rarely the hardest part; vision, scope, institutional knowledge, and leadership discipline were.

The Real Bottleneck Was Never Typing

For experienced operators, the hardest questions have never been purely syntactic. They are questions of context:

• What problem are we actually solving?
• What existing systems, standards, and dependencies must this change respect?
• What is the smallest safe version of the change?
• How will we know whether it worked?
• Who will own it when the original author—or the original AI tool—is no longer available?

AI can make implementation faster without making those questions easier. In fact, it can make them easier to avoid. When a plausible pull request arrives in minutes, organizations may feel pressure to approve it simply because the output exists.

AI Amplifies Organizational Behavior

Across teams experimenting with AI, practitioners describe a familiar mismatch: basic platform foundations such as DNS, SSO, and clear product direction remain unresolved while new MCP and agentic workflows are pursued. Teams can also accumulate redundant work, long reviews, and little shared understanding of why a change exists.

The important lesson is not that AI is incapable. It is that AI accelerates whatever operating model surrounds it. A disciplined team can use AI to pay down technical debt, automate maintenance, and improve internal tooling. An undisciplined organization can use the same capability to produce more overlapping systems, more exceptions, and more changes nobody feels responsible for.

The pattern is straightforward: AI does not create organizational dysfunction, but it can make that dysfunction faster to ship.

That is a leadership problem before it is a tooling problem. Leaders set the incentives, decide what counts as progress, and determine whether teams have time to review, test, instrument, and maintain what they ship.

DevOps Is the Evidence Layer

DevOps practitioners are often asked to translate a high-speed development mandate into a system that still behaves predictably. That means making reality visible:

• Which services and dependencies changed?
• Did latency, error rates, capacity, security posture, or cost move after the release?
• Can a team explain the behavior without asking the generating model to explain its own output?
• Does the change improve the customer or business outcome it was intended to improve?

This is where Splunk Observability helps. Rather than treating observability as a dashboard added after an incident, teams can use it as the feedback loop for AI-assisted delivery. Agent Observability in Splunk Observability Cloud brings metrics, traces, and logs together to evaluate agent behavior, observe performance across the entire AI stack, enable token usage and cost optimization, and control agents in real-time with runtime guardrails to block harmful actions and outputs for every response. .

Validate and Tune Telemetry Before Production With Observability Studio

Observability Studio is an open-source instrumentation sandbox that provides a visual, real-time environment for designing, testing, and validating OpenTelemetry data. Unlike traditional observability backends that only provide feedback post-ingestion, Observability Studio enables teams to verify telemetry accuracy during the development cycle, resulting in faster instrumentation velocity, higher data quality, and improved operational resilience across the enterprise.

Before an AI-assisted change reaches production, developers can use Observability Studio to establish a shared baseline: service health, deployment markers, SLOs, dependency behavior, and the infrastructure signals that matter for the workload. If the system includes AI agents, add agent-level latency, errors, token usage, estimated cost, quality, and risk signals.

The point is not to create another dashboard for its own sake. The point is to agree on what healthy looks like before the change arrives. Then, after deployment, the team can compare behavior instead of debating opinions.

For AI-backed applications, Splunk Agent Observability provides an agent graph, trace data, and OpenTelemetry GenAI instrumentation. Splunk Agent Observability extends that visibility with workflow traces, tool-call context, quality and safety evaluators, tokenomics, and runtime guardrails.

A Better Leadership Mandate

The right mandate is not “everyone must generate more code.” It is “teams may use AI to increase delivery capacity, but every change must remain explainable, observable, owned, and reversible.”

That changes the measures that matter. Commit volume is less useful than change failure rate. Ticket throughput is less useful than recovery time and customer impact. AI adoption is less useful than the organization’s ability to detect, understand, and correct the consequences of AI-assisted change.

Coding may be increasingly automated. Judgment, context, and accountability remain engineering work.

Try Observability Studio

Start with one AI-assisted service. Use Observability Studio to establish a baseline and make the signals visible to the people who own the outcome, and try the Free Edition of Observability Cloud and get the visibility you need to understand how your agents behave, perform, and consume resources as you move from experimentation toward production.