Using Reactive Agents in Kubernetes Architecture

If you’re building AI agents that need to work reliably in production, not just in demos, this is the full-stack setup I’ve found useful From routing to memory, planning to monitoring, here’s how the stack breaks down 👇 🧠 Agent Orchestration → Agent Router handles load balancing using consistent hashing, so tasks always go to the right agent → Task Planner uses HTN (Hierarchical Task Network) and MCTS to break big problems into smaller ones and optimize execution order → Memory Manager stores both episodic and semantic memory, with vector search to retrieve relevant past experiences → Tool Registry keeps track of what tools the agent can use and runs them in sandboxed environments with schema validation ⚙️ Agent Runtime → LLM Engine runs models with optimizations like FP8 quantization, speculative decoding (which speeds things up), and key-value caching → Function Calls are run asynchronously, with retry logic and schema validation to prevent invalid requests → Vector Store supports hybrid retrieval using ChromaDB and Qdrant, plus FAISS for fast similarity search → State Management lets agents recover from failures by saving checkpoints in Redis or S3 🧱 Infrastructure → Kubernetes auto-scales agents based on usage, including GPU-aware scheduling → Monitoring uses OpenTelemetry, Prometheus, and Grafana to track what agents are doing and detect anomalies → Message Queue (Kafka + Redis Streams) helps route tasks with prioritization and fallback handling → Storage uses PostgreSQL for metadata and S3 for storing large data, with encryption and backups enabled 🔁 Execution Flow Every agent follows this basic loop → Reason (analyze the context) → Act (use the right tool or function) → Observe (check the result) → Reflect (store it in memory for next time) Why this matters → Without a good memory system, agents forget everything between steps → Without planning, tasks get run in the wrong order, or not at all → Without proper observability, you can’t tell what’s working or why it failed → And without the right infrastructure, the whole thing breaks when usage scales If you’re building something similar, would love to hear how you’re thinking about memory, planning, or runtime optimization 〰️〰️〰️〰️ ♻️ Repost this so other AI Engineers can see it! 🔔Follow me (Aishwarya Srinivasan) for more AI insights, news, and educational resources 📙I write long-form technical blogs on substack, if you'd like deeper dives: https://lnkd.in/dpBNr6Jg

𝗦𝗲𝗹𝗳-𝗵𝗼𝘀𝘁 𝗖𝗹𝗮𝘂𝗱𝗲 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝘁 𝘀𝗰𝗮𝗹𝗲 — 𝗶𝗻𝘀𝗶𝗱𝗲 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝗞𝘂𝗯𝗲𝗿𝗻𝗲𝘁𝗲𝘀, 𝘄𝗶𝘁𝗵 𝗮𝗰𝗰𝗲𝘀𝘀 𝘁𝗼 𝘆𝗼𝘂𝗿 𝗼𝘄𝗻 𝘀𝘆𝘀𝘁𝗲𝗺𝘀. Meet 𝗸𝗼𝗺𝗽𝘂𝘁𝗲𝗿-𝗮𝗶 — an open-source, Kubernetes-native platform for running persistent Claude AI agents on your own infrastructure, inside your own private network, with direct access to your internal tools and data. Every agent is a first-class Kubernetes resource (a CRD). That means YAML manifests, kubectl, RBAC, namespaces, Helm — all the tools your team already uses for production workloads now apply to your AI agents. 𝗧𝘄𝗼 𝘄𝗮𝘆𝘀 𝘁𝗼 𝘂𝘀𝗲 𝗶𝘁 👇 𝟭. 𝗥𝘂𝗻 𝗖𝗹𝗮𝘂𝗱𝗲 𝗮𝗴𝗲𝗻𝘁𝘀 𝗶𝗻𝘀𝗶𝗱𝗲 𝘆𝗼𝘂𝗿 𝗶𝗻𝗳𝗿𝗮 — no third-party SaaS, no data leaving your cluster. Agents can reach your internal databases, APIs, Git repos, and MCP connectors (Slack, GitHub, Atlassian, Notion, Google Workspace) — because they live on the same network. 𝟮. 𝗕𝘂𝗶𝗹𝗱 𝘆𝗼𝘂𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗼𝗻 𝘁𝗼𝗽 — if your platform or app needs to run agents in the backend, don't reinvent the orchestration layer. Wrap komputer-ai with a simple SDK/API/CLI call and focus on your UX, auth, and business logic instead. 𝗪𝗵𝗮𝘁 𝘆𝗼𝘂 𝗴𝗲𝘁 👇 ✅ 𝗣𝗲𝗿𝘀𝗶𝘀𝘁𝗲𝗻𝘁 𝗮𝗴𝗲𝗻𝘁𝘀 with their own pod + workspace PVC — survives restarts, sleep cycles, and re-tasks ✅ 𝗠𝗮𝗻𝗮𝗴𝗲𝗿/𝘄𝗼𝗿𝗸𝗲𝗿 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 — managers spawn and coordinate sub-agents autonomously ✅ 𝗦𝘁𝗲𝗲𝗿 𝗺𝗶𝗱-𝘁𝗮𝘀𝗸 — redirect a running agent without restarting it ✅ 𝗖𝗿𝗼𝗻 𝘀𝗰𝗵𝗲𝗱𝘂𝗹𝗶𝗻𝗴, 𝘀𝗹𝗲𝗲𝗽𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀, 𝗮𝘂𝘁𝗼-𝗱𝗲𝗹𝗲𝘁𝗲 — full lifecycle control ✅ 𝗥𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗿𝗲𝘀𝘂𝗹𝘁𝘀 streamed over WebSocket — build chat UIs, dashboards, Slack bots in minutes ✅ 𝗖𝗼𝘀𝘁 𝘁𝗿𝗮𝗰𝗸𝗶𝗻𝗴 per task, per agent, per namespace ✅ 𝗦𝗗𝗞𝘀 for Python, Go, and TypeScript — plus a CLI and web dashboard 𝟯 𝘀𝘁𝗲𝗽𝘀 𝘁𝗼 𝗴𝗲𝘁 𝘀𝘁𝗮𝗿𝘁𝗲𝗱 👇 📌 helm install komputer-ai oci://https://lnkd.in/gZpJnWap 📌 kubectl apply -f agent.yaml — or use the CLI, UI, or any SDK 📌 Stream events in real time as your agent works 🌐 https://lnkd.in/gssGTm68 — star it, fork it, build on it.

In traditional Kubernetes autoscaling, scaling is often tied to CPU and memory thresholds. But real-world workloads don’t always spike in predictable patterns. We needed a way to scale based on external event metrics-like message queue length, API request rates, or database lag. That’s where KEDA (Kubernetes Event-Driven Autoscaler) came in. Real-World Implementation Use Case: Autoscale Kubernetes workloads based on custom metrics like Prometheus alerts, Kafka lag, and SQS message depth. Execution: Deployed KEDA as a lightweight controller in our EKS cluster Defined ScaledObjects with custom Prometheus queries as event sources Integrated with external systems (Kafka, Redis, AWS SQS, PostgreSQL) using KEDA scalers Tuned cooldown periods, polling intervals, and scale target thresholds per workload type Monitored metrics using Grafana, confirmed responsiveness in production spikes Used Metrics Server and Prometheus Adapter to bridge HPA requirements with KEDA triggers Benefits Realized Enabled fine-grained autoscaling for asynchronous and background jobs Reduced idle pod costs in low-traffic windows by over 60% Ensured instant scale-up during peak event load-no need for pre-provisioned buffers Centralized scaling logic into GitOps-managed ScaledObjects Achieved tighter alignment between actual demand and resource provisioning Event-driven scaling helped us optimize cost, performance, and resource efficiency in a unified Kubernetes-native model. Tools Used KEDA, Kubernetes, Prometheus, Metrics Server, Grafana, Kafka, SQS, Redis, PostgreSQL, ScaledObject, Helm #Kubernetes #KEDA #Autoscaling #EventDrivenArchitecture #SRE #CloudNative #Prometheus #Kafka #AWS #Redis #PostgreSQL #PlatformEngineering #GitOps #CI_CD #Helm #MetricsServer #JobSearch #Observability #SiteReliabilityEngineering #InfrastructureAsCode #Scalability #CloudEfficiency #TechCareers #SREJobs #DevOpsJobs #C2C

Agent architectures in the Real World ⬇️ 𝗪��𝗮𝘁'𝘀 𝘁𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺? There is plenty of advice on how to build agent prototypes that > use third-party API, like OpenAI or Antrhopic. > encapsulate all the agent + tooling logic inside a single Python program > run locally with docker compose But the problem is, this design DOES NOT scale. Meaning, if you are a company trying to use this blueprint, it will either work > too slow > too expensive, or > BOTH So the question is, how can you design agentic systems that are cost efficient (both in time and money?). Here is a blueprint. 𝗦𝘆𝘀𝘁𝗲𝗺 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 📐 > 𝗔𝗴𝗲𝗻𝘁 𝗔𝗣𝗜 𝗴𝗮𝘁𝗲𝘄𝗮𝘆 Routes incoming requests to the appropriate agent using a lightweight LLM. > 𝗔𝗴𝗲𝗻𝘁 𝗹𝗼𝗴𝗶𝗰 𝘀𝗲𝗿𝘃𝗶𝗰𝗲𝘀 A task-specific agent gets the task, and uses a step-wise workflow (e.g. Langgraph, CrewAI, Langchain...) that invokes one or several LLMs, and a set of external tools. > 𝗧𝗼𝗼𝗹 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 They act as an interface between your agents and the backend services these agents need to solve the task. Here you can use MCP clients and servers, and a library like FastMCP. > 𝗟𝗟𝗠 𝘀𝗲𝗿𝘃𝗲𝗿𝘀 They need to run on dedicated GPU nodes, using tools like vLLM or NVIDIA NIM. Every service is running as a containerised app in your Kubernetes cluster. BOOM! 𝗪𝗮𝗻𝗻𝗮 𝗹𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 𝗿𝗲𝗮𝗹 𝘄𝗼𝗿𝗹𝗱 𝗟𝗟𝗠𝗢𝗽𝘀? In the next weeks Marius Rugan and I will dig deeper into LLMOps system architecture. In public. For FREE. Follow Pau Labarta Bajo so you don't miss what is coming next

Where do AI agents actually live at runtime? 🤨 Everyone’s talking about multi-agent systems, but the deployment reality is often vague. “In the cloud” isn’t an architecture. Here’s the concrete picture: Agents are Dockerized microservices, running in Kubernetes or as serverless functions. Orchestrated via LangGraph, Temporal, or custom planners. Communicating through Kafka or gRPC. Triggered by events, APIs, or other agents. Backed by vector DBs and real-time memory stores. Governed by centralized registries and guardrails. It’s not magic. It’s system design. If we want agents to own business logic—decoupled from UI or app code—we need infrastructure that matches the autonomy we claim. The tech exists. 👇 Curious how you’re approaching this—are your agents already in production? #AIArchitecture #MultiAgentSystems #GenerativeAI #LLMops