Quick answer: Verify Iris is enabled in Project Settings + DefaultEngine.ini. Configure relevancy on the actor (always relevant, owner only, distance based) via IrisReplicationComponent.

A multiplayer game opts into Iris replication for its efficiency benefits. Some actors stop replicating to clients post-switch. The actors weren’t configured with Iris-aware relevancy.

Confirm Iris Active

Console:

Net.Iris.UseIrisReplication
> 1

Confirms Iris is on. If 0, enable in Project Settings → Engine → Iris.

Per-Class Config

Each AActor subclass can declare:

UCLASS(meta=(UseIrisReplication))
class AMyActor : public AActor
{
    AMyActor() {
        bReplicates = true;
        bAlwaysRelevant = false;   // or true for HUD pieces
    }
};

Iris also looks at the FNetIrisConfig in ClassRep tables. Generated at startup.

Relevancy in Iris

Iris uses ReplicationFilter and ReplicationSubObjects to decide reach. Default:

Set filter via subsystem during BeginPlay if you need non-default:

UReplicationSystem* RS = UE::Net::UReplicationSystem::Get();
RS->SetFilter(NetHandle, FNameSpatialFilter);

Mixed Iris/Classic Pitfall

Some classes default to classic in 5.3/5.4. Check ConfigureClassReplicationSystem; verify it routes to Iris for your class. Forcing classic on one actor while others use Iris causes confusing missing-replication symptoms.

Verify with Network Profiler

Window → Developer Tools → Network Profiler. Capture session; inspect per-actor replication frequency and size. Missing actors absent from the report.

Verifying

Server + clients. Actor appears, properties update, RPCs fire. Network profiler shows traffic for the actor. Latency under target despite Iris’s overhead.

Understanding the issue

Replication systems make the same data visible on multiple machines. The hard part is that these machines have different clocks, different network conditions, and different load. A replication bug usually means one of these realities was ignored.

The specific bug described above is the kind that surfaces during integration rather than unit testing. It depends on a combination of factors: the asset configuration, the runtime state, the platform's specific behavior. In isolation, each piece looks correct; in combination, the bug emerges. This is why thorough integration testing - playing the actual game in realistic conditions - catches things that automated tests miss.

Why this happens

Bugs of this class are particularly easy to ship past internal QA because they often depend on specific runtime conditions - hardware combinations, network states, or asset configurations that QA didn't reproduce. Players hit them in the wild, file reports that are hard to repro, and the bug accumulates negative reviews while engineering tries to recreate the failure mode.

At the engine level, the behavior comes from a deliberate design decision in Unreal. The engine team chose a particular trade-off - usually performance versus convenience, or generality versus specificity - and that trade-off has consequences when you push against it. Understanding the trade-off is what turns 'this bug is mysterious' into 'this bug is the expected consequence of this design'.

Verifying the fix

For shipping games, the safest verification is a staged rollout. Apply the fix to 1% of players for 24 hours; watch the affected metric; expand if green. Skipping the staged rollout means the verification is the entire player base, which is too high a stakes for most fixes.

Reproducibility is the prerequisite for verification. If you can't reliably reproduce the bug pre-fix, you can't reliably verify it post-fix. Spend time getting a clean reproduction before you write any fix code. The fix is fast once you understand the reproduction; the reproduction is the slow part.

Variations to watch for

There's almost always a less obvious case where the same problem applies. The reported case is the one a player hit; the related cases hide because they're rarer or affect fewer players. After fixing the reported case, search the codebase for the pattern - one fix often unlocks several.

Adjacent bugs often share a root cause. After fixing the case you've found, spend an hour searching the codebase for similar patterns. What's the same call with different arguments? The same data flow with a different entity type? The same lifecycle issue in a sibling system? Each match is a candidate for the same fix, or a related fix that prevents future bugs of the same class.

In production

Live games surface this bug class at scale. What's a rare edge case in development becomes a daily occurrence once you have a few thousand concurrent players. The class isn't 'this player has a unique setup'; it's 'one in N thousand sessions will trigger this exact combination'.

When triaging a similar issue in production, prioritize gathering data over hypothesizing causes. A player report describes a symptom; what you need is a build SHA, a session timestamp, and ideally a screen recording or session replay. With those, the bug becomes tractable. Without them, you're guessing at hypothetical reproductions that may not match what the player actually hit.

Performance considerations

If this issue manifests under high load (many actors, many particles, many network connections), profile the post-fix code path with realistic counts. The original cost was a bug; the new cost is real work, and real work has a budget.

Diagnostic approach

Before applying any fix, gather enough context to be confident you're addressing the actual cause and not a similar-looking symptom. The cheapest diagnostic step is reproducing the bug deterministically - if you can't get the same failure twice in a row, your fix attempts will be hard to evaluate. Lock down the reproduction first.

For Unreal-specific diagnostics, the editor's profiler is the canonical starting point. Capture a representative frame with the symptom present; compare against a frame without the symptom; the diff often points directly at the cause. If the symptom is non-deterministic, capture multiple frames and look for the pattern - the cause is usually a state transition or a specific input value rather than a continuous effect.

Tooling and ecosystem

The tooling around this bug class matters as much as the fix itself. Good logging, accessible profilers, and clear error messages turn 30-minute investigations into 5-minute ones. If your project doesn't have visibility into this code path, the first fix should add the visibility - the second fix uses it.

Within Unreal, the relevant diagnostic surfaces include the standard frame debugger, memory profiler, and engine-specific debug overlays. Each one shows a different facet of what's happening. The frame debugger reveals draw call ordering and state transitions; the memory profiler shows allocation patterns; the debug overlay reveals per-system state. Bugs that resist one tool usually surrender to another - the trick is knowing which tool to reach for first.

Edge cases and pitfalls

Platform-specific edge cases are worth enumerating explicitly. iOS handles backgrounding differently than Android; Windows handles focus changes differently than macOS. A fix that works on the development platform may not work on every target. Test on each shipping platform deliberately.

When writing a regression test for this fix, focus on the boundary conditions that surfaced the original bug. Tests that exercise the happy path catch obvious regressions; tests that exercise the boundary catch the subtler regressions that look like new bugs but are really the original returning. The latter are the tests that earn their keep over the long life of the project.

Team communication

When this bug class affects multiple teams (often the case for cross-system issues), early communication prevents duplicate work. The team that owns the symptom may not own the cause. A 15-minute conversation at the start of triage often saves hours of independent investigation.

If this fix touches a system several engineers work in, a short writeup in the team's engineering channel helps. Not a full design doc - a paragraph explaining what was wrong, what's fixed, and what to watch for. Future engineers encountering similar symptoms will search for the fix; making it findable is a small investment that pays back later.

“Iris is opt-in per project. Verify the flag, then verify relevancy. Two layers, both required.”

Iris shines for many-player games. For sub-16-player Co-op, classic is often sufficient and easier to debug — only adopt Iris when scale demands it.