Canary and shadow deployment let you roll out an autonomous AI agent without betting production on it. Shadow mirrors live traffic to the new agent but discards its outputs and suppresses side effects, so you validate behavior at zero blast radius. Canary then routes a small, growing percentage of real traffic (5%, 25%, 50%) behind explicit promotion gates. For agents that perform real writes or tool calls, pair both with policy simulation, idempotent retries, approval gates, and an audit trail so every promotion or rollback is provable.
The production problem
Agent releases often fail for one reason: teams validate prompts, then skip runtime rollout discipline. The first real traffic spike becomes the experiment.
Shadow traffic catches some logic regressions. It does not validate irreversible side effects, approval latency, or policy-deny drift under live constraints.
Canary without measurable gates has the same flaw. You get staged exposure, but no deterministic promotion rule.
What top results miss
| Source | Strong coverage | Missing piece |
|---|---|---|
| Argo Rollouts Canary | Concrete canary mechanics (`setWeight`, `pause`, surge/unavailable controls, and optional traffic routing). | No policy-risk decision layer for autonomous agent side effects. |
| Google Cloud Deploy: Canary strategy | Clear phased rollout model with explicit percentage stages and per-phase progression semantics. | No guidance for autonomous-agent side effects, approval backlog gates, or replay-safe promotion. |
| Istio Traffic Mirroring | Concrete shadow-traffic pattern by mirroring live requests to a candidate service without serving mirrored responses. | No rollout scoring, policy simulation, or governance gate model for autonomous agent execution. |
Rollout model
Promotion should be a state machine with explicit entry and exit conditions. If a stage fails, rollback must be mechanical.
| Stage | Traffic profile | Promotion gate | Rollback trigger |
|---|---|---|---|
| Shadow stage | 0% user-facing actions, mirrored evaluation workload | Policy simulation pass + no critical schema/output violations | Any safety deny spike or parser failure over baseline |
| Canary stage 1 | 5-10% live traffic | Score >=95 or manual hold if 75-94 with >=50 data points | Error rate delta >2x baseline or approval backlog breach |
| Canary stage 2 | 25-50% live traffic | Stable latency + policy deny rate within allowed threshold | Sustained overload reason codes (`pool_overloaded`, `no_workers`) |
| Full promotion | 100% | No critical incidents through one full business cycle | Immediate rerun-from-step/rollback workflow on severe regression |
Cordum runtime implications
| Implication | Current behavior | Why it matters |
|---|---|---|
| Pre-deploy policy validation | `POST /api/v1/policy/simulate` and `POST /api/v1/policy/bundles/{id}/simulate` | Canary candidates can be evaluated without side effects before traffic shift. |
| Workflow dry-run | `POST /api/v1/workflows/{id}/dry-run` | Rollout steps can be tested with environment context before live dispatch. |
| Safe rerun path | Workflow supports rerun-from-step and dry-run mode | Rollback and corrective rollout can resume from known safe boundaries. |
| Run idempotency | Runs support `Idempotency-Key` on creation | Promotion retries do not create duplicate rollout runs. |
| Approval gate support | Unified approvals endpoint for workflow and policy approvals | High-risk rollout stages can require explicit human authorization. |
Implementation examples
Canary traffic stages (YAML)
apiVersion: argoproj.io/v1alpha1
kind: Rollout
spec:
strategy:
canary:
steps:
- setWeight: 10
- pause: { duration: 20m }
- setWeight: 25
- pause: { duration: 30m }
- setWeight: 50
- pause: { duration: 30m }Promotion scoring policy (YAML)
rollout_gates:
min_data_points_per_metric: 50
canary_lifetime_hours: 3
metric_interval_hours: 1
score:
pass: 95
marginal: 75
rollback_triggers:
max_error_rate_delta: 2.0
max_policy_deny_rate: 0.03Cordum pre-promotion checks (bash)
# 1) Simulate policy on rollout candidate
curl -sS -X POST http://localhost:8081/api/v1/policy/simulate -H "X-API-Key: $CORDUM_API_KEY" -H "X-Tenant-ID: default" -H "Content-Type: application/json" -d '{"topic":"job.prod.deploy","tenant":"default"}'
# 2) Dry-run workflow before canary shift
curl -sS -X POST http://localhost:8081/api/v1/workflows/WF_ID/dry-run -H "X-API-Key: $CORDUM_API_KEY" -H "X-Tenant-ID: default" -H "Content-Type: application/json" -d '{"input":{"release":"v1.8.0"},"environment":"staging"}'Limitations and tradeoffs
- - Shadow traffic increases infra cost and does not fully validate side-effect safety.
- - Slower canary stages reduce blast radius but delay feature delivery.
- - Strict promotion gates reduce bad releases but may produce false positives on noisy metrics.
- - Manual approval steps reduce risk but add coordination latency.
Frequently asked questions
What is the difference between canary and shadow deployment for AI agents?
Shadow deployment mirrors live requests to a candidate agent but discards its outputs and suppresses side effects, so you compare behavior without acting on it. Canary deployment routes a small, growing percentage of real traffic (for example 5%, 25%, 50%) to the new agent and lets it execute real actions under tight monitoring. Shadow validates logic safely with zero blast radius; canary validates real side effects on a controlled slice of traffic. For autonomous agents that perform writes or tool calls, use shadow first to catch logic regressions, then canary behind policy gates to catch side-effect and approval-latency problems.
How do you validate an AI agent safely before sending it real traffic?
Validate the policy decision and the workflow plan before any traffic shift. In Cordum, POST /api/v1/policy/simulate (and /api/v1/policy/bundles/{id}/simulate) evaluates a candidate against policy with no side effects, returning the safety decision (ALLOW, DENY, REQUIRE_APPROVAL, or ALLOW_WITH_CONSTRAINTS). POST /api/v1/workflows/{id}/dry-run executes the workflow plan with environment context but without live dispatch. Together they let you confirm a candidate will not be denied at the safety kernel and that its workflow steps resolve correctly before the first user-facing request.
Why isn't a canary percentage rollout enough for autonomous agents?
A canary percentage controls exposure but not promotion logic. Without explicit numeric gates — minimum sample size, a pass/marginal score threshold, and rollback deltas — promotion becomes an intuition call. Autonomous agents add a second gap: they cause irreversible side effects, so a regression on a 5% canary can still write bad data or trigger real external actions. You need replay-safe execution (idempotency keys so retries don't duplicate actions), approval gates on high-risk stages, and an audit trail that records every safety decision, so a bad canary is both detectable and reversible.
How does Cordum make agent rollout side effects reversible and auditable?
Cordum gates every dispatch through the Safety Kernel before a worker runs, so a canary candidate is evaluated against policy on the hot path, not after the fact. Runs accept an Idempotency-Key on creation so promotion retries do not create duplicate actions, and the workflow engine supports rerun-from-step and dry-run mode so corrective rollouts resume from a known-safe boundary. Every decision, scan, approval, and override is recorded in the audit trail and surfaced through the governance timeline, which gives you the evidence to promote or roll back with confidence.
Next step
Run this in one sprint:
- 1. Define rollout stages (shadow, 10%, 25%, 50%, 100%) per critical workflow.
- 2. Set numeric gates (sample size, score thresholds, rollback deltas) in policy.
- 3. Wire policy simulation and workflow dry-run into your release pipeline.
- 4. Require explicit approval for high-risk promotion steps.
The promotion gates above are only as good as the policy behind them. See AI Agent Governance for how Cordum enforces policy decisions, approval gates, and immutable audit trails before agents execute. Then continue with AI Agent Policy Simulation and AI Agent Fail-Open vs Fail-Closed.