Most advice on engineering handoffs focuses on execution. “Make them warmer,” we hear. Write better documentation, establish clearer communication protocols, conduct more thorough transition meetings. It’s not bad advice, but it assumes that handoffs are inevitable fixtures of software development that just need a better process.
That assumption deserves some scrutiny.
Handoffs exist because organizations make specific structural decisions about role boundaries, ownership models, and quality gates. Those decisions create weak spots where knowledge degrades and time accumulates. Recent research on open source software development demonstrates that knowledge loss measurably impacts productivity, with the effect amplified by system complexity.
Most organizations respond by focusing on making handoffs "warmer.” But when we question why handoffs exist (and whether they should), we reveal leverage points that process improvements alone can't address.
Most engineering leaders think of handoffs between teams — PM to engineering, developer to reviewer, code to QA. The first handoff actually happens earlier: when a user describes their problem to someone in your organization. That initial transfer sets the knowledge degradation in motion.
Start with a user who encounters a problem. In most organizations, they can't speak directly to engineers. They describe their need to a CS rep or product manager, who interprets and documents it.
This first handoff already introduces interpretation layers. Nonaka and Takeuchi's framework on knowledge creation explains why: tacit knowledge — the user's actual experience, context, and unstated assumptions — can't be captured in writing. The moment you convert it to a ticket or spec, you strip away nuance like a game of Telephone. And nuance is very important in software development.
The PM hands documented requirements to engineering leadership. Estimation follows, often by people who won't build the feature, and the work enters a backlog and waits.
Meanwhile, information rots during queue time — context fades; market conditions shift. By the time a developer picks up the work, the original problem may have evolved.
Development triggers its own handoff sequence. A developer completes code and submits a pull request. In teams with deep specialization, only 2-3 people out of 13 might have the expertise to review certain changes. The PR waits in queue.
When the reviewer finally picks it up, they lack full context about the feature's business purpose and the developer's implementation decisions. They review based on code quality and architectural compliance, potentially missing user experience issues that someone closer to the original problem would catch
QA receives the build next. They test against written specifications, which themselves are abstractions of the user's actual need. When QA finds defects, the cycle repeats — back to the developer, who has to rebuild the context they had weeks ago.
User acceptance testing happens months after the initial request. The user sees something that may (or may not) technically match the spec, but may no longer solve their evolved problem.
Production deployment then introduces a final handoff. Support teams receive the feature with limited context about implementation decisions or known edge cases. When issues arise, they have to reconstruct the chain backwards.
With apologies to Ali Bati
These handoff patterns function as leading indicators within the Velocity dimension of Uplevel’s WAVE Framework, because handoffs directly impact delivery speed. Measuring wait times between handoffs reveals where knowledge transfer breaks down and where coordination overhead accumulates — both of which slow delivery regardless of how fast individual contributors work.
Specialization delivers genuine value. A security expert identifies vulnerabilities that generalists miss. A performance engineer spots optimization opportunities invisible to others. Deep domain knowledge produces higher-quality work within specific domains.
But the coordination cost is real. When only two people can review infrastructure code, those two people become bottlenecks. Every infrastructure change queues until they have capacity. The team can't parallelize this work, and the specialists can't scale their expertise fast enough to match the team's growth.
Research on software development lifecycles shows that work spends less than 10% of time in active states in most organizations. The remaining 90%+ consists of wait time: queuing for resources, waiting for approvals, sitting in backlog reviews. Handoffs drive this inefficiency. Each transfer point creates a queue because specialists have finite capacity and context switches incur costs.
The knowledge loss compounds. When a specialized reviewer finally examines code, they lack the context the original developer had. They now have to invest time understanding intent. Questions arise. The developer context-switches back to answer them. The work bounces between active and waiting states, accumulating delay with each transfer.
Organizations face a genuine tradeoff. Deep specialization improves quality within domains but increases handoff complexity and wait times. We know that specialization brings value to an organization. The real question is whether the quality gains justify the costs.
Many organizations optimized for specialization depth without accounting for the coordination overhead it creates — and the lack of accounting is the problem.
Extreme Programming (XP), introduced by Kent Beck in the 1990s, took a structural approach to handoffs. Pair programming eliminated the PR review handoff by having two developers write code together. Both maintained full context, so no knowledge transfer was needed.
Integrated product thinking removed the PM translation layer by embedding product expertise directly in development. Cross-functional teams meant fewer boundaries to cross.
The DevOps movement applied similar thinking to deployment. Werner Vogels' principle "you build it, you run it" eliminated the operations handoff by making developers responsible for production support — a structural change rather than a process improvement.
These practices remain rare 25+ years after introduction. Why? Adoption requires organizational changes that make people uncomfortable. Pair programming challenges individual ownership norms and productivity measurement systems. It’s often a bit awkward and a little slow, and it requires some vulnerability and humility. Similarly, embedded product roles disrupt established hierarchies. Shifting left on QA means rethinking continuous integration and delivery. It’s hard, and it’s human nature to avoid hard things.
But the rarity doesn't invalidate the approach. Organizations that eliminate handoffs (thoughtfully!) outperform those that optimize them. The question is whether leadership can make the structural changes required. Can every engineering org adopt XP? No. But you can likely start by thinking critically about which handoffs actually help and which are just an organizational reflex.
Effective diagnosis starts with understanding why specific handoffs exist. These questions surface whether handoffs represent necessary tradeoffs or organizational baggage:
Many organizations assume product managers translate between users and engineers because engineers can't or should not talk to users directly. This assumption creates the PM handoff. Test it. What actually happens if engineers do talk to users? The answer reveals whether the boundary serves genuine needs or perpetuates organizational habits.
QA handoffs often exist because organizations assume developers can't ensure quality themselves. The separate QA role guards against low-quality releases.
But this assumes quality can be “inspected in” rather than built in. Teams with high CI maturity, automated testing, and shift-left quality practices demonstrate that separate QA handoffs address symptoms rather than causes.
Velocity metrics like cycle time by phase can reveal which handoffs create the biggest bottlenecks. A three-day PR review wait time could indicate insufficient reviewer capacity, over-specialized knowledge, or a host of other root causes. A two-week QA queue could mean inadequate QA staffing or too-large batches entering the testing phase.
Having the data to diagnose and understand where time accumulates shows which handoffs impose the highest costs.
Some specializations justify the tradeoffs in velocity. Security reviews by dedicated experts often prevent problems that generalists would miss. But many handoffs exist because organizations optimized for depth without accounting for breadth (the advantage of hiring more generalists).
If the bottleneck cost outweighs the benefit, that’s an opportunity to distribute knowledge: pair specialists with non-specialists on reviews, rotate engineers through the specialized domain, create explicit teaching time, or hire for breadth rather than depth on the next round.
Engineering handoffs appear as execution problems in team retros:
These symptoms suggest team-level fixes — better processes, clearer documentation, more people.
The actual causes live in organizational design. Leadership chooses (or inherits) role definitions, quality ownership models, acceptable CI/CD practices, and team boundaries, and those choices create the handoff points. When organizations optimize for individual function performance — the 10x developers or the rockstar product managers — they create coordination overhead that shows up as handoff delays.
Leadership typically doesn't see handoff problems directly. Status reports show work items moving through stages. What these views miss is the accumulated wait time and knowledge degradation.
The right metrics make handoff costs visible at the system level. When lead time averages 30 days but active work time totals 3 days, the remaining 27 days represents time spent waiting for one reason or another. When quality dips and features fail UAT, those nuances reveal how knowledge degrades in the long journey from user need to released software.
Addressing these issues requires leadership action. Teams can “warm up” handoff execution, but they can't remove handoffs that org design created. Engineering leaders have the opportunity and the responsibility to examine current structures. Do they serve actual needs or preserve historical decisions that no longer apply?
Handoffs are a perfect example of how technical solutions alone can't solve organizational problems. You can implement perfect CI/CD pipelines and measure every metric, but if organizational structure creates knowledge silos and bottlenecks, velocity and value delivery will hit a ceiling.
Changing handoff patterns means changing human behaviors and incentives. When you eliminate a QA handoff, you're asking developers to own quality differently. When you remove the PM translation layer, you're redefining how product and engineering collaborate. When you reduce specialized review bottlenecks, you're distributing expertise (and potentially threatening established hierarchies).
Data can make handoff problems more visible, but measurement alone doesn't drive organizational change. That requires understanding the human dynamics behind the numbers and guiding teams through structural transformation. See how Uplevel approaches engineering effectiveness as a sociotechnical challenge.