Welcome to hyper-agility, here is your guide to maintain sanity

In the last 50 years we went from punch cards to assembly, to object-oriented programming, higher-level languages, and frameworks that made building software faster with each iteration. Every step changed what “good engineering” looked like day-to-day.

AI-assisted coding is another step in that line, but it changes the pressure points. It speeds up code production, but it also speeds up mistakes, scope drift, and confusion. It also makes it easier to generate a lot of code without having the same level of confidence you would have if you wrote it yourself.

Using AI to build software is like using a slot machine for an IDE. We are essentially navigating a set of variables to control a probabilistic output. We work with prompts, context modulation, guardrails, tools, and coding standards, all to define the space in which our solution will take shape. It works, and it's incredibly powerful — but the results are not always perfect and trusting the process does not come easy.

This is where the tension shows up. A lot of our processes were shaped by the fact that writing and changing code was expensive, so we optimized for predictability and controlled change. With AI, writing the code stops being the bottleneck, but correctness, safety, and coordination do not get cheaper. When the rate of change goes up, the cost of uncertainty shows up faster, and the stress does too. AI coding burnout is real, partly because you can move quickly while still feeling unsure about what you are shipping.

Most of us now have power tools on the desk. The goal is to use them without hurting ourselves, breaking production, or turning teamwork into a mess. This guide is about keeping the speed where it helps, and adding the guardrails where it matters.

How AI is making current processes harder

Current development processes grew around a simple constraint. Writing code took time and cost money, so everything around it was optimized to avoid waste and reduce risk.

We learned to write as little code as possible, to keep it maintainable so we would not have to rewrite it, and to build MVPs and POCs to check whether the code was worth writing at all. Agile ceremonies, prioritization meetings, planning poker, and t-shirt sizes all exist for the same reason. They are tools to make delivery more predictable when code is expensive.

A lot of the existing processes still work, because they were designed to optimize everything except typing code. Faster code generation should, in theory, make those processes even more effective. In practice, something else breaks first.

The main issue is confidence. When code comes out faster than it can be understood, reviewed, and trusted, the old signals stop working. The age of “LGTM” reviews is gone. Small iterative pull requests are easy to review, but with AI they often feel like artificial speed limits.

At the same time, even a small change can carry a large and poorly understood blast radius. Most of us have already seen what happens when that goes wrong. Tools happily refactor more than intended, delete the wrong files, or “fix” a problem by breaking something else. The problem is not that these tools exist. It is that the process around them was built for a slower, more predictable kind of change, and that mismatch shows up as stress, hesitation, and a constant sense that something might go very wrong at any moment.

A new process

The mechanics of writing code changed faster than the processes around it, and the gap is starting to show. Building reliable systems is still hard. What changed is how quickly we can get into trouble when things are unclear or unchecked. Right now, a lot of teams are forcing old habits to fit a new velocity, and the friction is obvious.

Many of the problems described here are not new. Large, unclear changes, weak reviews, fragile tests, and burnout all existed long before AI-assisted development. What AI changes is the rate at which these failures occur and the scale of their impact.

The goal is not to throw everything away. Many existing practices still do their job well. Others add cost without reducing risk at today’s pace. The work now is to keep what holds up, and to adjust what no longer matches how we actually build software.

HyperAgility is a set of guidelines rather than a strict process. It's a way to navigate AI-assisted coding to increase efficiency, but more than that, to prevent burnout and to reduce the stress of working with AI.

Prerequisites and constraints

HyperAgility assumes a minimum level of organizational hygiene: clear ownership, basic testing discipline, and the ability to say “no” to unsafe changes.

This only works with clear ownership. AI does not change responsibility. Someone still owns the code, the design decisions, and what happens in production. In practice, higher velocity makes that ownership more important, because mistakes spread faster and uncertainty shows up sooner. HyperAgility is meant to help teams move quickly without losing accountability, not to create a way to ship risky changes and blame the tool when things break.

HyperAgility should be adopted selectively and defensively, not as an operating model.

HyperAgility Guide

Spec Driven Development

It might feel counterintuitive that the solution to HyperAgility is what is regarded as the death of efficiency, waterfall development. And I'm not arguing that we need to plan out six months in advance, not even close to that. What we do need to take from waterfall design though is the extensive design of features before we start implementing.

Nobody wants to spend weeks manually defining every detail of a feature, and at higher velocity that is not realistic. This is where AI can help in a different way. AI tools can be used to scan existing codebases, documentation, APIs, and constraints to quickly produce higher-fidelity specifications, surface assumptions, and highlight gaps that would otherwise be missed.

When requirements, specifications, or instructions are unclear or underspecified, AI systems will naturally attempt to fill in the gaps. This often manifests as “creativity”: adding functionality that was not requested, making assumptions about behavior, or even building features that were never intended or that cannot realistically be supported.

When given an incomplete problem definition, the model will complete it. The larger the ambiguity surface, the more room there is for hallucination, scope expansion, and unintended behavior.

It is important to distinguish this from creativity used deliberately. There is real value in allowing AI to explore solution spaces, suggest alternatives, or propose new approaches. But that exploration must happen in a controlled phase, before code generation begins.

Creativity belongs in feature shaping, design exploration, and problem definition, not during implementation.

This does not mean that all vagueness can be eliminated. Some uncertainty is real and unavoidable, especially early in product development. What matters is that vagueness is contained and made explicit, rather than leaking into implementation by accident.

To achieve that, uncertainty must be deliberately contained and explicitly tracked as it moves toward implementation. This means being explicit not only about what needs to be built, but also:

• how it should be built,
• what constraints apply,
• what is explicitly out of scope,
• and what trade-offs are acceptable.

That will not always be fully achievable. At the very least, unclear or unspecified parts of the implementation process should be acknowledged explicitly, and a conscious decision should be made about whether the AI is allowed to fill in those gaps, and under what guidance.

With human developers, vagueness is often manageable. Humans apply critical thinking, notice when something drifts beyond scope, and operate at a relatively low output scale. If a human starts going in the wrong direction, the damage is limited and visible early.

AI operates very differently. It can generate massive amounts of code at high speed, which means that even small ambiguities can quickly expand into large, deeply embedded changes. Vagueness effectively creates an attack surface in the development process—a space where anything can happen. If left unchecked, the model will hallucinate into that space by design.

Requirements Gathering

Lately, it feels like everyone is a coder. And while it can be incredibly frustrating when your CEO vibe-coded an app the other night that suddenly becomes the new top priority, this shift can also be used to good effect when handled carefully.

Software is never built in a vacuum. It only has value when it has users, and the people closest to those users are often not the ones writing production code. Historically, requirements have been filtered through layers of translation: sales to product, product to engineering, engineering back to product, and so on. That bridge between technical and non-technical roles has long been a source of frustration, misunderstandings, missed expectations, delayed releases, awkward client conversations, and burnout for developers.

Sales teams, in particular, are famously good at selling things that do not yet exist, and then asking engineering to make them real. Most teams have lived through the same pattern: rushing a proof of concept or half-baked feature just so it can be demoed to a potential customer. “It doesn’t need to be perfect, they just need to see how it works.”

AI changes this dynamic, but it does not remove the underlying risks.

Instead of outright resisting non-technical stakeholders building apps, teams can choose to channel that energy in a controlled way. Rather than having sales or business teams present wireframes, slides, or incomplete designs, which will happen regardless, they can be trained to use AI tools to generate working proof-of-concept applications that illustrate how customer needs might be addressed.

These applications must be treated strictly as napkin designs. They are not canonical implementations, reference architectures, or production-ready code. They do not imply feasibility, scalability, or correctness. Their value lies in communication, not execution. As artifacts, they are more expressive than wireframes and more concrete than written descriptions, but they remain sketches.

Any such input still needs to go through proper feature shaping and specification before implementation begins. A working demo does not replace that process. What it can do is improve the handoff between sales, product, and engineering by anchoring discussions in observable behavior rather than abstract intent, and by reducing ambiguity around what is actually being asked for.

Architecture and Design

When it comes to architecture, or more specifically code organization, traditional software development relied on a set of patterns and practices designed primarily around readability and maintainability. Code was written by humans, read by humans, and modified by humans. Because of that, the main goal was to make sure developers could quickly understand the system, reason about it, and safely make changes. Maintainability was the name of the game, and most architectural patterns evolved to serve that goal.

In a hyper-agile, AI-driven development model, that assumption no longer holds. AI systems are now responsible for producing and modifying large portions of the codebase. Humans remain accountable for outcomes, but they are no longer the primary agents performing each change. This shifts what architecture needs to optimize for.

Architecture becomes a problem of enabling safe automated change. The goal is not to maximize elegance or reuse, but to minimize the amount of context required to make a correct modification. Code needs to be organized so that AI-assisted workflows can operate on well-defined, bounded areas of the system without pulling in unrelated concerns or accidentally affecting distant behavior.

In hyper-agility, architecture is about making intent and boundaries mechanically obvious. The system should clearly communicate what belongs together, what does not, and what can be changed independently. This is as important for humans reviewing changes as it is for AI systems generating them.

While architecture must support safe automated change, it cannot do so at the expense of human comprehension under pressure. Systems are ultimately debugged, operated, and defended by humans, often under time constraints and partial information. In the end, for the foreseeable future, it will still be a human debugging a production incident in the middle of the night.

Organizing code by domains works well in this environment. Domain-driven design provides semantic separation between parts of the system. When domains and their boundaries are explicit, changes can often be limited to a smaller surface area, which reduces the amount of context required and improves the reliability of generated changes.

Alongside domain separation, strong typing becomes increasingly important. Statically typed languages provide explicit contracts that make data structures, interfaces, and function boundaries clear. In environments where static typing is not available, consistent use of type hints, schema validation, and explicit function contracts helps reduce ambiguity. The goal is to make expectations visible, for humans and for AI-assisted tooling.

Another consequence of optimizing for automated modification is a higher tolerance for localized duplication. Shared abstractions and deep reuse increase coupling and expand the context required to make a change safely. In AI-assisted workflows, that additional context increases risk. Keeping logic local, even at the cost of duplication, often makes systems easier to modify, reason about, and regenerate. Of course, duplication raises the risk of business logic divergence and should be treated carefully.

This does not mean abandoning structure or discipline. It means choosing structure that favors containment, explicitness, and replaceability over cleverness. Architecture in hyper-agility is less about crafting perfect designs and more about creating systems that can be changed repeatedly, at scale, with predictable outcomes.

Testing

AI-assisted development changes the shape of risk. It dramatically increases the scope of individual changes and makes it easier to introduce unintended behavior far away from the original intent. A single pull request can touch thousands of lines of code across multiple repositories, often reshaping behavior in ways that are difficult to predict by reading the diff alone.

This is where regression testing becomes critical.

With AI-generated code, testing is less about validating that a feature works in isolation and more about confirming that existing behavior still holds. Regression and integration tests become the primary way to detect when a change, even a small one, alters system behavior outside its intended scope. This does not make testing easier. It makes it more demanding. AI-assisted development increases the amount of code that needs to be validated, while also making it easier to generate low-quality tests that provide false confidence.

This shift also exposes maintainability issues. AI-generated code often accumulates complexity over time. Even when the code behaves correctly at first, it can be harder to reason about, refactor, or safely modify. Under these conditions, regression testing acts as a continuous feedback loop, making unintended behavior visible before it reaches production.

Unit testing especially requires more attention than usual. AI-generated unit tests often achieve high coverage with very little impact; they will generally feature heavy mocking and shallow assertions that tend to confirm the shape of the code rather than the behavior of the system. Unit tests need to be treated as features rather than artifacts. Good unit tests should be built to protect critical behavior of the application, testing happy and error paths and ensuring that changes in business logic don't lead to unintended consequences.

As systems evolve more quickly and with broader changes, testing pressure moves up the stack. Integration and regression tests that validate observable system behavior become the main defense against unintended change. Unit tests complement this by locking down critical logic and invariants, not by chasing coverage numbers.

Anything that can be reliably detected by compilers, linters, static analyzers, and AI-assisted code analysis should be handled at that level. This reduces noise in the test suite and keeps automated tests focused on behavior rather than structure.

When systems are built and changed at this scale, testing is what restores confidence. As the surface area of change grows, the ability to quickly see what broke, what did not, and why becomes more important than knowing exactly how the code is written. More importantly, tests decay quickly, and hyper-agile change accelerates that decay. Keeping a test suite meaningful requires explicit ownership, periodic pruning, and treating flaky or low-signal tests as defects.

Code Quality

In an AI-driven, hyper-agile development environment, code quality becomes a conscious trade-off rather than an absolute goal applied uniformly across the codebase. Many traditional ideas of code quality, such as perfect readability, elegant abstractions, or optimizing for long-term maintainability everywhere, do not scale well with the velocity and frequency of AI-generated change. Much of today’s software is constantly evolving rather than remaining stable for long periods.

This does not mean that code quality stops mattering. Certain standards remain non-negotiable and form a hard baseline. Correctness is mandatory, not just at the level of individual functions, but at the level of system behavior and invariants. Team conventions still matter, especially where they encode architectural intent, ownership boundaries, or safety constraints.

Type systems and schemas are part of that baseline, whether through static typing, strict type hints, or explicit runtime validation. Contracts between components need to be clear and enforced. For systems with tighter constraints, this also includes memory safety, correct resource management, well-defined concurrency behavior, and explicit handling of failure cases.

For mission-critical or high-risk systems, the baseline is higher. Predictable performance characteristics, bounded resource usage, safe error handling, and correct behavior under partial failure or unexpected input all remain required. AI-assisted development does not relax these requirements and often increases the need to make them explicit.

Beyond that baseline, code quality needs to be evaluated pragmatically and in context. If a feature behaves correctly, follows agreed standards, fulfills its requirements, and passes regression and integration tests, it should generally be considered acceptable, even if the implementation is not particularly elegant. In areas likely to change again soon, heavy optimization for structure or readability often slows teams down without providing proportional value.

Pragmatism does not mean accepting opaque or fragile code in core paths. Areas involving security, data integrity, concurrency, or irreversible side effects require higher standards regardless of velocity. HyperAgility relaxes standards selectively, not uniformly.

Code Reviews

Code reviews are one of the biggest challenges and pain points in hyper-agile, AI-driven software development. A large part of this comes down to developer confidence. Developers still do not fully trust AI-generated code, and for good reason. Scope creep, unintended security changes, broken code paths, and subtle behavioral regressions are common issues.

The core problem is scale. Reviewing 200 lines of code is manageable. Reviewing 5,000 lines of AI-generated code is a nightmare. Traditional line-by-line review techniques simply do not work at this volume, which means code reviews themselves need to change.

In hyper-agility, code reviews need to operate at a higher level, which requires better review tooling. Reviewers need fast visibility into what actually changed, not just where lines moved. The first step of a review should be a structured overview that highlights the most important aspects of the change, such as:

• code quality and maintainability concerns
• data model changes
• behavioral changes
• changes to contracts between services, APIs, or interfaces
• major architectural shifts

These signals need to be visible immediately so that a reviewer can quickly understand what actually changed before deciding where to go deeper.

Many of the review approaches described here depend on tooling that is still immature or unevenly available. Until those tools mature, teams must cap change size based on what reviewers can realistically understand, not on what AI can generate.

As an alternative, stacking PRs can also work - the developer using AI will generate the changes in a single session but to make review easier, will create several stacked PRs with incremental changes and a milestone / checkpoint system.

AI works best with a stable and complete context. Breaking a feature into many small PRs often forces repeated re-alignment through specifications and guardrails, increasing overhead and introducing inconsistencies. A single, larger PR preserves context and intent, but only if reviewers are given the right tools to understand it.

The goal of HyperAgility is not to defend large PRs for the sake of AI, but to highlight the need for better tools to review them. For this approach to work, code reviews need to focus on early risk detection. Any issue introduced by a change, whether related to security, architecture, data integrity, or system behavior, needs to be surfaced as early and as clearly as possible. The goal is not to read every line of code, but to understand whether the change aligns with the intended outcome and whether it introduces unacceptable risk.

At this scale, code reviews become less about exhaustive inspection and more about quickly identifying the impact of a change and deciding where human attention is actually needed.

Development Process and Pull Requests

When it comes to the development process, hyper-agility tends to favor an approach that can feel counterintuitive at first: building large, complete features first, and iterating on them afterward.

Instead of splitting feature development into many small pull requests, it is often more effective to implement an entire feature in a single, larger PR using one consistent AI context. This makes it easier for the initial implementation to follow the original specifications, intent, and architectural guidelines. AI performs best when it can reason over a complete problem with stable context, and breaking work into many small steps increases the risk of losing that context and making inconsistent decisions over time.

This is a statement about implementation, not about release. A complete feature can still be guarded, tested, and exposed gradually. The value of a larger initial PR is that it establishes a coherent baseline that reflects the full intent of the change.

Once that baseline is in place, refinements, fixes, and improvements can be handled through smaller, incremental commits. This preserves velocity while still allowing teams to correct issues, respond to feedback, and improve the implementation as confidence grows.

Building features exclusively through small, incremental PRs often introduces what amounts to context rot. Each step requires intent, constraints, and design decisions to be re-established, which increases overhead and frequently results in weaker or less consistent implementations. In hyper-agile environments, it is often more effective to accept an imperfect initial implementation and improve it iteratively than to fragment development and lose coherence.

Working this way shifts the development process toward prioritizing context continuity first, with refinement and optimization happening afterward. Of course, the other prerequisites of HyperAgility like tooling, processes, ownership, and code hygiene need to be in place for the process to truly work. Large PRs work only when blast radius is controlled, rollback is feasible, review capacity exists, and ownership is clear.

Teamwork

While the use of AI in software engineering has become increasingly common, most development tools and workflows are still poorly adapted to team-based, AI-assisted development. Many AI coding tools are optimized for individual productivity rather than coordinated work across multiple engineers. As a result, introducing AI into team environments often creates new sources of friction instead of removing them.

One of the main challenges is asymmetric velocity. Different team members achieve very different speeds depending on how they use AI tools, which can lead to imbalance and coordination problems. This is not just a productivity issue, but a team dynamic issue. Without care, these differences can affect planning, collaboration, and morale. Accountability can also become less clear. When AI is involved, it is easy to attribute mistakes to the tool, which makes ownership harder to reason about unless responsibilities are made explicit.

Every significant change needs a clear human owner, regardless of how much code was generated by AI. Ownership does not mean writing every line by hand. It means being responsible for the intent, the impact, and the follow-up when something goes wrong. Without this, teams drift toward a state where speed is rewarded and cleanup is implicit, which quickly leads to resentment and burnout.

Merge conflicts are another growing issue. Because AI can generate large volumes of code quickly, the size and scope of changes per pull request tend to increase. Working in parallel becomes more difficult, even in systems that are well decoupled. Multiple features will inevitably touch overlapping areas, and traditional parallelization strategies struggle at this scale.

One way to address this is to use AI as an early warning system rather than a decision-maker. Merge conflict prediction during feature shaping and pull request creation can surface risks earlier. When a new PR is opened, an AI system can analyze other open work, identify overlapping files, domains, or behavioral changes, and highlight where interaction is likely. This gives teams a chance to coordinate and adjust before conflicts become costly, rather than reacting after the fact.

Beyond parallel work, clear coding standards and guardrails become essential. Uniformity is difficult to maintain when prompts, models, and agents vary across team members. Without strong constraints, codebases lose cohesion quickly. Well-defined feature specifications, project-level guardrails, and up-to-date documentation help counteract this, especially when paired with strong review practices and clear ownership.

Feature shaping also needs to be treated as a team activity rather than an individual one. Doing more work upfront to align on intent, scope, risks, and interactions pays off later, particularly in high-velocity environments. Understanding how one feature affects another reduces friction and improves collaboration.

In practice, making AI work effectively in teams depends on a few recurring elements:

• clear and shared feature specifications
• strong guardrails and coding standards
• review and merge workflows that scale with larger changes
• ongoing communication during feature creation

Maintaining a shared library of prompts for common tasks can also help improve consistency. Even when the same prompt and context produce different results across runs or models, shared prompts provide a common starting point and reduce unnecessary divergence.

Uneven velocity, large overlapping changes, and unclear ownership tend to show up when AI-assisted coding is adopted carelessly across a team. When some people move much faster than others, and the tooling is not integrated into shared workflows, coordination breaks down and responsibility becomes harder to track. HyperAgility only works when AI use is visible, cohesive, and aligned with how the team actually works.

Feature Flags

Building a major feature used to take weeks, sometimes even months. During that time, engineering teams would gradually become familiar with the feature as it evolved. They would observe its behavior, test both happy and unhappy paths, and develop an intuitive understanding of how it fit into the system. That time dimension was critical because it created a natural feedback loop that allowed confidence to build alongside the implementation.

One of the main reasons developer confidence is lower in AI-driven development, and one of the things that actually hurts velocity the most, is velocity itself. Features can now be built in hours or days. Combined with past experiences of AI-generated code introducing subtle issues, the lack of elapsed time since a feature’s creation does not inspire confidence. Even when the code appears sound and tests pass, complex systems still need time and exposure to reveal edge cases, usability issues, and unintended behavior.

This is where feature flags become important.

In a hyper-agile environment, releasing a newly built and lightly exercised feature to the entire user base immediately introduces unnecessary risk. Even with strong upfront planning and thorough regression and integration testing, things can still go wrong. More importantly, there is no feedback more valuable than observing how real users interact with a feature.

Because features are ready so quickly, it is common that very few people, including developers, have spent meaningful time actually using them before release. Small issues, rough edges, and opportunities for improvement often only become visible once the feature is used in real conditions.

Ideally, teams would dog-food new functionality internally and give engineers, product managers, and account managers time to live with the change before a full rollout. In practice, this is often difficult to achieve consistently. Feature flags provide a practical alternative by allowing controlled exposure.

By releasing features behind flags to a limited audience, teams can observe real behavior, gather feedback, monitor metrics, and iterate safely, without putting the entire system or user base at risk when introducing a new change.

An important caveat to keep in mind is that feature flags are sometimes hard to implement and introduce operational complexity. They need to be actively managed and ultimately expired when the feature is ready to be released. This adds overhead to the development team. Ideally a mix of feature flags, delayed releases, and extensive user acceptance should be used to increase confidence in features.

New Metrics for an AI-Driven Development Process

The industry has struggled to measure developer productivity for decades, and AI has made that problem more difficult rather than easier. Traditional metrics like velocity provided at least a rough signal of progress. While velocity still matters to some extent, it is no longer sufficient on its own. When code generation becomes cheap and fast, speed stops being the primary bottleneck, which also makes it a weaker signal of how well things are actually going.

In a hyper-agile, AI-assisted environment, metrics need to shift away from how fast code is produced and toward how efficiently the system turns intent into stable, working software.

Teams should still aim to perform well, but performance needs to be evaluated at the level of the development system as a whole. One of the most useful dimensions to observe is how much friction exists between specification and production. This includes signals such as how many iterations are required before a feature reaches an acceptable state, how often features need to be partially or fully regenerated, how frequently AI-generated changes introduce regressions or unexpected behavior, and how often merged changes trigger follow-up fixes.

These signals act as proxies for confidence and stability. A system that produces features quickly but requires constant rework is not operating efficiently. It is simply fast at producing output that does not hold up over time.

Specifications, guardrails, tooling, and AI configuration all influence these outcomes, but they are not measured in isolation. Their quality is reflected indirectly in how smoothly work moves through the system. Fewer retries, fewer surprises, and fewer corrective cycles indicate that the system is doing its job.

Over time, this makes it possible to improve the development process itself rather than optimizing individual components in isolation. The goal is not to maximize output, but to minimize friction, rework, and risk as ideas move from definition to production.

Additionally, given the increased capacity to develop new features, organizations need to start measuring the quality of the product management signals. When any feature can be built faster, the system runs the risk of becoming a mix of one-time use features, adaptations to edge use cases, and entire modules that need to be decommissioned. With great power comes great responsibility and the ability to build things fast needs to be kept in check.

Conclusion

HyperAgility is not about chasing speed for its own sake. Speed is already here. AI has changed how quickly software can be built, and teams are experiencing increasing pressure to adopt AI-assisted development, even when they choose to move cautiously or selectively.

In many ways, software development has become harder. Code is produced faster, but confidence is harder to earn. Quality is harder to judge. Small mistakes can spread further, and unpredictable behavior shows up more often. At the same time, competitive pressure keeps increasing, and the expectation to move fast never really lets up. The result for many developers is constant tension, uncertainty, and burnout.

The ideas in this document are not about throwing away what worked before. They are about shifting attention to where the real problems now live. Being more deliberate before implementation. Designing systems that make change easier to reason about. Treating testing as a way to understand behavior over time, not just as a checkbox. Rethinking reviews, pull requests, and teamwork so they scale with the volume of change.

HyperAgility is also about sustainability. AI can be incredibly productive, but without the right guardrails and tools, it turns into pressure rather than progress. The goal is to reduce cognitive load, restore confidence, and make quality visible again, not to ask people to simply work faster.

This is not a finished process or a rigid framework. HyperAgility is an early way of thinking about how teams can stay effective and healthy while building software in an environment that keeps accelerating.

What is missing from this document

At this point HyperAgility does not include specific tools and integrations like MCP servers, AI skills, coding agents, and efficient workflows. The current landscape is evolving at a very fast pace and developer preferences are highly subjective and context dependent.

Things that HyperAgility will not prevent

HyperAgility cannot prevent, and in some cases will even exacerbate some issues:

• specifications that confidently encode wrong assumptions
• large PRs that bypass scrutiny due to review fatigue
• duplicated logic that silently diverges
• ownership that exists on paper but not in practice

Just as the challenges and solutions that we are addressing in this document are not new, neither are the above issues; they are all part of the effort of building reliable, scalable, and usable software systems.