The Quality Imperative: How Agentic Coding Can Elevate Software Beyond Throughput

The mainstream discourse surrounding agentic coding has largely centered on throughput—promises of shipping faster, writing more code, and achieving more with fewer human resources. This narrative has dominated organizational adoption strategies, positioning AI tools primarily as labor multipliers that accelerate development cycles. Yet within this conversation lies an unexplored dimension, one that Luca Palmieri articulates with refreshing clarity: agentic coding's potential to fundamentally elevate quality standards in systems where reliability isn't just desirable but essential.

In critical infrastructure—payment processing rails, database systems, control planes for cloud infrastructure—quality translates directly to economic value. These systems demand high availability, predictable performance, and unwavering trustworthiness. The quality metrics aren't abstract; they can be quantified in dollars saved or earned. Palmieri, drawing from extensive experience building such systems, suggests that agentic coding isn't merely about accelerating existing practices but about enabling entirely new approaches to quality assurance that were previously economically infeasible.

The Economic Shift: From Code to Verification

The most profound implication of agentic coding isn't that it makes coding faster, but that it fundamentally alters the economic equation of software development. Traditional software engineering processes and rituals have long been built upon a single, core assumption: code is expensive. Creating software requires specialized expertise, unpredictable time investments, and carries significant risks when requirements evolve mid-project.

Agentic coding challenges this premise at its foundation. Code generation becomes remarkably cheap, while verification emerges as the new dominant cost center. This inversion creates constraints but also opportunities. The challenge isn't generating code anymore but ensuring its correctness—a problem Palmieri suggests can be addressed through tighter feedback loops and more sophisticated verification infrastructure.

This economic shift creates a particularly interesting quadrant where agentic workflows offer substantial advantages with minimal downsides: time-consuming work coupled with cheap verification, and problems where partial or approximate solutions still deliver value. Within this space lie countless quality-improving tasks that languished in backlogs when every line required manual effort but now become economically viable.

Five Quality-Enhancing Workflows Enabled by Agentic Coding

Palmieri illustrates this potential through five concrete examples drawn from his own experiments, each demonstrating how agentic coding changes the calculus of quality-improving work:

1. Building More Tooling

Most engineering organizations accumulate a backlog of valuable but low-priority tooling: quality metrics to track, code patterns to enforce, safeguards to implement. These items rarely justify diverting attention from feature work, despite their long-term value for system health. With agentic coding, the equation changes dramatically.

Consider Palmieri's example of a CLI tool that tracks the ratio of safe to unsafe code on a crate-by-crate basis. A task that might have occupied weeks of an engineer's time can now be implemented with minimal input, followed by straightforward verification. The resulting tool continuously improves system quality by providing visibility into code safety metrics that would otherwise remain invisible. This pattern extends to countless other tooling opportunities: linter improvements, static analysis extensions, test coverage utilities—all previously deemed not worth the investment but now suddenly accessible.

2. Prototyping to Discover Constraints

Engineering management has long harbored a recurring dream: the perfect specification that anticipates all constraints and challenges before implementation begins. This waterfall mentality has seen a resurgence in the AI age under the banner of "spec-driven development," where detailed specifications precede agent execution.

Palmieri proposes an alternative: leveraging agentic coding to push iterative prototyping further. Rather than attempting comprehensive upfront design, the approach involves just enough initial planning to launch an agentic experiment. The agent's failures and confusions become valuable data points, revealing constraints the original design failed to account for. This process—observe, document, refine, repeat—effectively maps the problem space through executable probes.

The advantage of this approach lies in its confrontation with reality. While careful upfront analysis might eventually uncover design flaws, cheap executable probes reveal them earlier in the process. Each failed attempt surfaces concrete issues: unexpected API behaviors, unforeseen concurrency problems, or technical debt that obstructs progress. This method doesn't eliminate the need for design but makes it more responsive to actual system constraints.

3. Building to Compare

In traditional development, when multiple design options exist, teams typically resort to theoretical analysis—whiteboard discussions, estimation of pros and cons, and selection based on the most persuasive argument rather than empirical evidence. The economic cost of implementing multiple approaches usually prohibits such experimentation.

Agentic coding changes this dynamic by making prototype development inexpensive. Palmieri illustrates this with his work on a tree-based data structure where the server process forks, requiring identification of nodes across fork boundaries. He had agents implement three different approaches: using memory addresses, position within the tree, or introducing a node index concept. The empirical comparison revealed that the arena-based implementation offered the best combination of safety, simplicity, and performance—a conclusion that might not have emerged from theoretical analysis alone.

This approach injects empirical data into architectural decisions, reducing reliance on intuition and organizational politics. The ability to quickly prototype and compare multiple options transforms decision-making from a largely social process into a more evidence-based practice.

4. Implementing Low Value-Per-Line Abstractions

Some abstractions provide significant system value but require tedious implementation with low value-per-line of code. Palmieri offers the example of approximately 2,000 lines of Rust code creating a safe interface over Redis' RedisModule_Reply* FFI functions. While the resulting abstraction minimizes unsafe code downstream and ensures correct handling of array and map lengths, no sane developer would hand-write such repetitive code—the return on investment would be prohibitively low.

Yet with agentic coding, this implementation becomes economically viable. The agent can generate the tedious boilerplate code while the engineer focuses on designing the interface and verifying its correctness. This pattern extends to countless other domains: protocol implementations, data validation layers, translation utilities between formats—all providing system value disproportionate to the implementation effort when performed manually but suddenly accessible through AI assistance.

5. Paying Off Tech Debt Eagerly

Small tech debt items have traditionally languished because addressing them requires diverting attention from feature work while delivering immediate but limited value. With agentic coding, this equation shifts dramatically.

Agentic coding works most effectively with closed feedback loops: the agent generates code, automated checks verify it, and the agent iterates until verification passes. Tightening this loop increases trust in the output. This reframes small tech debt items as investments in verification infrastructure. Tasks like tightening type constraints, improving test coverage, or enabling new static analyses—previously "nice to have" but rarely prioritized—become both easy to execute and clearly valuable.

Palmieri cites the example of migrating small chunks of C code to Rust to bring more code under Rust's borrow-checker and miri (a tool for catching undefined behavior). Each migration represents a small, manageable task for an agent, but the cumulative effect compounds, creating more code subject to static analysis and providing more reliable signals about change correctness. This virtuous cycle accelerates both human and agent development over time.

The Future of Quality Engineering

These examples aren't isolated curiosities but indicators of a broader shift in software engineering practices. As Palmieri notes, he offers no grand theory about the profession's future, but suggests that agentic coding neither diminishes the importance of software engineering nor erodes the value of craft. Instead, it raises the bar on engineering discipline.

In systems where quality matters, organizations will necessarily invest more in verification, tooling, and feedback loops to extract real value from agentic workflows. This isn't a reduction in engineering rigor but an elevation of it. The focus shifts from manual code production to designing systems that can effectively verify and improve automatically generated code.

The implications extend beyond technical practices into organizational structures and career development. Engineers will need to develop new skills centered around verification system design, test strategy, and quality infrastructure. The most valuable engineers may increasingly be those who can design effective feedback loops and create verification mechanisms rather than those who can most quickly generate code.

Conclusion

Agentic coding's most profound impact may ultimately be not on throughput but on quality. By dramatically reducing the cost of code generation while making verification the new economic constraint, it enables quality-improving practices previously deemed economically infeasible. The resulting systems—more thoroughly instrumented, more rigorously prototyped, more comprehensively compared, and more systematically maintained—represent a significant advancement in software reliability.

This isn't to suggest that agentic coding represents a silver bullet for quality challenges. The fundamental difficulties of software complexity, changing requirements, and human factors remain. But by expanding the range of economically viable quality-improving practices, agentic coding offers a new pathway to more reliable, more trustworthy software systems.

As we continue to explore the frontiers of AI-assisted development, Palmieri's perspective reminds us that the most valuable applications may not be those that help us do more of what we already do, but those that enable us to do things we previously couldn't justify. In the realm of software quality, this frontier is just beginning to reveal itself.

For those interested in exploring these ideas further, Palmieri's work connects to several important concepts in modern software engineering:

• The Outcome Engineering manifesto emphasizes that "Failures are Artifacts," treating failed experiments as valuable data points rather than setbacks
• The principles discussed resonate with those in "Engineering Rigor in the LLM Age" by Bryan Cantrill, Adam Leventhal, Rain Paharia, and David Crespo
• Tools like miri for detecting undefined behavior become increasingly valuable as more code generation enters the development process

The exploration of agentic coding's potential to enhance software quality represents not just a technical evolution but a philosophical one—shifting our focus from production to verification, from intuition to empirical evidence, and from heroics to systematic improvement.