Most code execution systems work perfectly — until they hit production.
A simple runner. A Docker wrapper. A sandbox endpoint.
Everything looks fine in staging.
Then production traffic arrives:
One tenant spikes CPU.
Another hits memory limits.
Logs become unreadable.
Observability disappears.
Someone accidentally enables outbound networking.
And suddenly “code execution” isn’t a feature anymore.
It’s infrastructure.
The Hidden Problem With Code Execution
If your platform runs dynamic workloads — CI tasks, internal automation, user scripts, AI agents — you are executing untrusted or semi-trusted code.
That changes everything.
Execution must handle:
Multi-tenancy
Isolation
Deterministic resource enforcement
Governance
Observability
Long-term stability guarantees
Otherwise, you don’t have a feature.
You have a liability.
What Breaks First in Production
1️⃣ Shared State
If environments are reused carelessly:
Temp files leak
Memory isn't reclaimed cleanly
Execution artifacts cross tenant boundaries
The fix? One container per execution. Ephemeral lifecycle. No state persistence.
Every run starts clean. Every run ends destroyed.
2️⃣ Weak Resource Limits
If limits are advisory instead of enforced at runtime:
One tenant starves the node
Memory spikes cascade
PID exhaustion kills the host
Production-grade enforcement looks like this:
Yaml
Copiar código
network_mode: none
read_only: true
cap_drop:
- ALL pids_limit: 64 mem_limit: 512m cpus: 0.5 security_opt:
- no-new-privileges:true Not optional. Default. 3️⃣ Network Enabled by Default Outbound networking during execution introduces: Data exfiltration risks Compliance headaches Dependency chaos In secure environments, execution should default to: Copiar código
network_mode: none
If networking is needed, it should be explicit and scoped.
4️⃣ Multi-Tenancy That’s Just “Tags”
Adding tenant_id to logs is not multi-tenancy.
Real multi-tenant execution requires:
Mandatory tenant identity in tokens
Per-tenant rate limits
Per-tenant resource ceilings
Tenant-aware quota enforcement
Explicit rejection paths
Enforcement, not labeling.
Execution as Infrastructure
The turning point happens when you realize:
Execution is not a helper utility.
It is a substrate.
That means it needs:
Deterministic lifecycle (start → run → collect → destroy)
Strong isolation defaults
Capability-minimal containers
Built-in observability
Governance and version guarantees
Without governance, execution becomes unpredictable over time.
And unpredictability is the enemy of platform engineering.
Governance Is Not Optional
Most execution systems focus on sandboxing.
Few define:
Strict semantic versioning
Backward compatibility guarantees
Deprecation lifecycle
LTS support
Formal support matrix
Security response timelines
But once execution is embedded in internal platforms, those guarantees matter more than raw performance.
If you can’t answer:
What breaks in the next major version?
How long is LTS supported?
What is the escalation window for a container escape?
Then you’re not operating infrastructure yet.
Enterprise Reality
In the enterprise edition of GozoLite, execution is treated as governed infrastructure:
Ephemeral containers per run
Network disabled by default
Capability drop baseline
Strict resource ceilings
Tenant-aware enforcement
Structured logging and metrics
Formal versioning and deprecation policy
Defined escalation model
Not because it’s fancy.
Because production demands it.
Final Thought
If your platform executes dynamic code — internal automation, CI, AI agents, user workloads — you are operating an isolation boundary.
Treat it like one.
Execution is not just about running code.
It’s about controlling blast radius.
And blast radius control is infrastructure work.
Top comments (0)