We architect agentic Go microservices on Kubernetes. Webhook payloads must be idempotent. Rate limits enforce predictable scaling.
Each agent is a stateless Go service with its own gRPC and HTTP endpoints. Webhook payloads arrive via cloud events. We enforce strict idempotency keys. Retries with exponential backoff are backed by a Redis-based deduplication layer. Rate limits apply per agent per tenant. We use a token bucket algorithm with a distributed Redis counter. For data sync, we deploy change data capture (CDC) pipelines. Debezium Kafka Connect streams SQL changes into Kafka topics. Agents consume from topic-specific consumer groups. State is stored in PostgreSQL with optimistic locking. Kubernetes Horizontal Pod Autoscaler scales agents based on CPU and custom metrics (e.g., pending webhook queue depth). All inter-service calls are via mTLS. Service mesh (Istio) handles traffic split for canary deployments.
Key constraint: webhook payloads can arrive out of order. We use a version vector (Lamport clocks) for each entity. Agents process only if payload version > current state. Stale payloads are dropped. No ordering guarantees needed. Rate limit responses include Retry-After headers. The API gateway (Kong) enforces per-tenant quotas before traffic hits the services.
Every agent action is authenticated via a signed JWT. The JWT contains a subject, tenant ID, and role. Authorization uses Role-Based Access Control (RBAC) with attribute-based conditions. We manage roles via Kubernetes Custom Resource Definitions (CRDs). A policy engine (Open Policy Agent) evaluates requests against policies stored in a GitOps repository. For service-to-service calls, we use SPIFFE identities in Istio. Workloads get their own X.509 certificates rotated every 12 hours. Permissions for data sync streams are managed via Kafka ACLs. Producers and consumers have only the topics they need. IAM for human operators uses Kubernetes RBAC with impersonation. No static secrets. Vault injects temporary credentials into pods via a mutating webhook.
Edge case: what if a webhook payload includes a token with insufficient permissions? The agent returns a 403 with a structured error. The payload is not processed but may be retried. Rate limits still apply to prevent abuse. We audit every authorization decision. Audit logs go to a separate secure bucket. Retention is one year.
We don't enforce order. Each payload carries a version vector (Lamport clock). The agent compares it with the stored state. Payloads with older or equal versions are ignored. This works because business logic is designed to be commutative for the most part. For operations that require strict ordering (e.g., balance transfers), we use a Saga pattern with a coordinator.
The API gateway returns HTTP 429 with a Retry-After header. The agent logs the rate limit event. No penalty is applied to the tenant's quota for the current window. We use a sliding window counter to smooth bursts. Retry logic in the client (webhook sender) should back off exponentially. We provide a client library that implements standard retry semantics.
The original JWT is passed in a custom header (X-Senrok-Identity). Downstream services verify the JWT using the public key set published by the authentication service. For asynchronous calls (e.g., Kafka), the producer includes a signed claim about the original subject. The consumer validates the signature and enforces RBAC. The entire chain is auditable.
Engineering services
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