Comparison
Cordum vs Custom Middleware
Declarative centralized policy engine vs fragile per-service if-statements: the governance gap teams outgrow.
Custom middleware starts simple: a few if-statements checking agent inputs before processing. As agent count and policy complexity grow, these scattered checks become a maintenance and consistency liability.
This page helps teams searching for cordum vs custom middleware understand when to move from ad-hoc code to a centralized governance engine.
| Evaluation Area | Cordum | Custom Middleware |
|---|---|---|
| Policy Model | Declarative, centralized policy bundles evaluated by the Safety Kernel. Policies are version-controlled, hot-reloaded, and apply uniformly across all agents. | Imperative if-statements and validation logic scattered across service code. Each service implements its own checks independently. |
| Decision Outcomes | Four structured outcomes: ALLOW, DENY, REQUIRE_APPROVAL, ALLOW_WITH_CONSTRAINTS. Constrained execution and approval workflows are first-class. | Typically binary: pass or fail. Approval workflows and constrained execution require significant custom engineering. |
| Consistency | Single policy engine enforces identical rules across all agents, services, and runtimes. Policy changes propagate instantly via hot-reload. | Policy logic duplicated across services. Changes require updating and deploying each service independently. Drift is common. |
| Output Safety | Built-in output safety layer: ALLOW, REDACT, or QUARANTINE results post-execution. Applied consistently to all agent outputs. | Output filtering requires custom code per endpoint. Redaction and quarantine semantics must be invented per service. |
| Audit Trail | Structured run timeline with policy decisions, reasoning, approval records, and evidence pointers. Queryable by run, actor, and policy rule. | Audit depends on custom logging. No standard format for policy decisions, reasoning, or approval chains across services. |
| Maintenance Burden | Policy-as-data: update bundles without code changes or redeployment. Simulation mode for testing policy changes before rollout. | Every policy change requires code changes, code review, testing, and redeployment. No simulation capability. |
Decision checklist
- Are you managing more than a handful of agents or policy rules across multiple services?
- Do policy changes require code deploys across multiple services today?
- Is policy drift between services causing inconsistent enforcement?
- Do you need structured audit trails and compliance-ready decision logs?
- Would simulation mode for testing policy changes before rollout reduce risk?
Related comparisons
Replace scattered checks with centralized governance
See how Cordum's declarative policy engine eliminates middleware sprawl with version-controlled bundles and hot-reload.