Generic automation tools miss something: every industry has its own rules, documents, and workflows. AgentForge fixes that. Domain-specific AI workflows with embedded business logic and enterprise connectors. Medical billing. Contract review. Invoice processing. CV screening.

Enterprise software is about to be built by AI agents. But they have no marketplace to trade with each other. Built AgentForge — a self-operating marketplace where autonomous agents build, test, and sell productivity tools to each other. Zero humans. Zero friction.

Every other AI platform hands you an answer. AgentForge shows you how it got there — every classification, every routing decision, every draft. No black boxes. No surprises. agentforgena.polsia.app

Building AI agents onchain shouldn't require a blockchain PhD. AgentForge is a no-code platform that lets you create, deploy, and monetize AI agents onchain in minutes. Pre-built templates. Smart contract tools. Tokenize your agents. The AI crypto ecosystem needed this.

SaaS dev is still bottlenecked by humans at every step — sprint planning, code reviews, deployment windows. Built AgentForge to eliminate that entirely. Autonomous agents handle the entire lifecycle — building, deploying, monitoring, optimizing — 24/7. Ship faster.

Humans need sleep. PTO. Occasional therapy. AgentForge agents don't. Hire one. agentforgena.polsia.app

Cursor, Harvey, Sierra, Glean — each does one thing. None do all of it. AgentForge agents work across your whole business — research, ops, support, growth — and show their work. Stop trading one vendor lock-in for another. agentforgena.polsia.app

Most AI agent platforms sell you tools. We built AgentForge to sell you results. A marketplace of specialized autonomous agents that research, draft, and ship completed proposals — 24/7, without oversight. You pay for what they deliver, not what they cost to run. Live soon.

Software development has a validation problem. Most micro-SaaS products take months to build and fail in weeks. AgentForge changes that — monitors markets, generates products, deploys them, and iterates based on what users actually do. Autonomous. Continuous.

Looking for bulk offer accessai according acloud against agate agentbuilder agentforge aireviews alis appointed association attached brought cfi controlled covered discussed ecloud fluxon foreign fuzion genix genova gensyn gives held increasing interested ionix multicloud nexthome orbitus placed possibility prepared proposed quantumx related removal removed reported smartoffice studies suggested technova tekno testlab toon travelagent upwork vcloud wix zentro

ODYSSEUS is live. 🧭 Your AI Life Navigator for building a healthier, wealthier, more intentional life. From fitness and nutrition to personal finance, productivity, habits, purpose and long-term fulfilment, ODYSSEUS helps you navigate life's journey with wisdom, discipline, and clarity. Built in minutes using the new VeWorld AI Suite - an incredibly intuitive platform that makes creating, launching and monetising AI agents accessible to everyone. The future isn't just using AI. It's forging your own. veworld.com/ #VeWorldAI #VeChain #AIAgents #AgentForge #PersonalGrowth #Longevity #FinancialFreedom #BuildOnVeChain $VET 🚀🧭

You don't need employees. You need agents. Build, launch, and manage a full automation agency — without recruiting, onboarding, or managing anyone. AgentForge: agentforge-1782805028107-w2y…

Agencies try to scale by hiring more humans. AgentForge scales you differently: spin up multiple specialized AI agents working 24/7, each self-reporting to one dashboard. Content shop, data service, design studio — all running without a single new hire. Live soon.

3 days into my AI evals restart. The part the original post skipped: what 'hands-on, from scratch, no agents' actually means in practice. The commitment: implement, don't import. Build the primitives, not the framework. So I'm working through Hamel's LLM Evals blog end to end, implementing each pattern against real systems I already have. AgentForge for agent traces. C3alabs deployments for regression tests. MemexLLM for confidence calibration. The work isn't new. The discipline of writing it down and measuring it is new. The realization I keep hitting: Most of what I shipped in the last year had eval-shaped holes. I had fallback paths without measurement. I had confidence thresholds that were never calibrated. I had event logs that I never diffed. The patterns were in the code. The eval discipline was not. The hard part isn't building the primitives. I can ship a grader in a weekend. The hard part is defining 'good' before you build it. Hamel's blog has dozens of patterns. I've barely scratched the surface. The negative space (what did the system fail to do, and can you prove it) s the real eval, and it's the part I've been skipping for months. This is the work. The thread is the receipt.

AgentForge v1.4 Sneak peek: New Tui!! Checkout the repo : github.com/MohitGoyal09/Agen… Detail Article :

I’ve spent the last 6 months building two production-grade Kubernetes platforms from scratch — not labs, not tutorials, but environments that run real workloads 24/7, handle real traffic, and solve real operational problems. Here’s what that looks like under the hood. PLATFORM 1: GKE Terraform Full GitOps I designed a complete GCP environment around Google Kubernetes Engine, provisioned entirely with modular Terraform. The network is a custom dual-stack VPC (IPv4/IPv6) with private subnets, strict NetworkPolicies, and zero public ingress for internal services. The cluster runs three distinct node pools: • Standard e2-standard-2 nodes for general workloads • SPOT L4 GPUs for low-latency inference • SPOT RTX 6000 Pro nodes for heavy reasoning workloads Everything ships through Git. FluxCD bootstraps the entire stack — infrastructure controllers, application manifests, secrets, and Helm releases. Kustomize manages overlays. SOPS encrypts every secret with Age, and Flux decrypts them during deployment automatically. The application layer is a multi-tenant AI platform (AgentForge) with dual-tier vLLM inference: • Tier 1 — L4 SPOT 24/7 running a Qwen coder model for fast tool-calling triage • Tier 2 — RTX 6000 Pro SPOT, KEDA scale-to-zero, running a larger Qwen model with fp8 KV cache for multi-file reasoning Each tenant gets isolated Hermes agent profiles, dedicated PostgreSQL databases, and independent observability. I also run a FastAPI waitlist API, an autonomous news quant pipeline that scrapes 371 feeds, N8N automation, and automated local-business web deployments. The data layer is a CloudNative PG fleet: four HA PostgreSQL clusters across seven databases, automated GCS backups, 7–30 day retention, and failover. Strimzi Kafka handles event-driven workloads. Security isn’t afterthought — it’s architecture: • Cilium CNI with eBPF datapath Hubble L7 observability • Strict NetworkPolicies namespace isolation • Tailscale-only access for monitoring and databases • Automated security pipeline: a sec-ops agent scans for CVEs and misconfigurations, confirmed findings auto-dispatch a backend-dev agent to patch them — audit to fix in one GitOps cycle • Every container runs non-root with enforced resource limits Cost discipline is built in: 60–90% GPU savings through SPOT instances, KEDA scale-to-zero, scheduled CronJobs, and strict CPU/memory caps. PLATFORM 2: Kubeadm Multi-Cloud Advanced Networking My second cluster is completely different self-hosted with kubeadm across 3 nodes (1 control plane, 2 workers), giving me direct control over the entire stack. The networking story here is the highlight. Cilium 1.16.6 is configured with: • BGP control plane for dynamic route advertisement • L2 announcements for cross-node LoadBalancer failover • A shared LoadBalancer IP pool for high availability This cluster runs diverse, real workloads: • Zcash blockchain node (zebrad) lightwallet with StatefulSets and gRPC/P2P • Prosody XMPP server handling C2S, S2S, and HTTPS • Linkding bookmark manager with persistent storage • Multipaper Minecraft server cluster: master Velocity proxy MariaDB (50Gi) plugins The data layer includes a 3-node HA CloudNative PG cluster, dedicated MariaDB for Minecraft, and Local Path Provisioner for dynamic PVs. GitOps is identical in philosophy to the GKE lab: Flux 2.7.2, Kustomize overlays, Renovate for automated dependency updates, SOPS-encrypted secrets, Hubble observability, and AlertManager for routing. What I’ve Learned These two platforms taught me something most tutorials can’t: the difference between “I followed a guide” and “I own this system.” The GKE lab taught me platform engineering at scale — how to optimize inference costs, design multi-tenant isolation, and build self-healing security pipelines. The kubeadm lab taught me what’s under the hood — control plane components, CNI internals, BGP in Kubernetes, and how to make bare-metal-style networking

🚀 Just crossed 997 commits on my cloud-native DevOps lab — and it’s easily the most ambitious infrastructure project I’ve ever built solo. I wanted a production-grade environment to sharpen my craft, so I went all-in on GKE, Terraform, and GitOps — not a tutorial cluster, but a full multi-tenant platform that runs real workloads 24/7. Here’s the stack: ☁️ **Infrastructure:** Terraform modules provisioning a dual-stack (IPv4/IPv6) VPC, private networking, and three node pools — standard e2, SPOT L4 for fast inference, and SPOT RTX 6000 for deep reasoning. 🔄 **GitOps:** FluxCD 1.7.6 Kustomize. Every manifest, database cluster, and network policy flows through Git. Secrets stay encrypted with SOPS Age and decrypt automatically during deployment. 🔒 **Zero-trust networking:** Cilium CNI with eBPF datapath, Hubble observability, and strict NetworkPolicies. Internal services (databases, monitoring, agent infrastructure) are Tailscale-only — zero public ingress. 🤖 **Multi-tenant AI (AgentForge):** The centerpiece. A private AI agent workspace with dual-tier vLLM inference: • Tier 1 — L4 SPOT 24/7 running a Qwen coder model for lightning-fast triage and tool-calling • Tier 2 — RTX 6000 Pro SPOT, KEDA scale-to-zero, running a larger Qwen model with fp8 KV cache for multi-file reasoning Each tenant gets isolated Hermes agent profiles, dedicated PostgreSQL databases, and its own observability — all orchestrated through a shared Kanban. 📊 **Data layer:** CloudNative PG fleet managing four PostgreSQL clusters across seven databases, automated GCS backups, and HA failover. 📈 **Observability:** Prometheus Grafana, accessible only via Tailscale. Every container has resource limits. Hubble gives L7 network visibility inside the cluster. 🔐 **Security automation:** A sec-ops agent continuously scans for CVEs and misconfigurations. When something’s confirmed, a backend-dev agent is auto-dispatched to patch it — audit to fix in one GitOps cycle. 💰 **Cost discipline:** 60–90% GPU savings through SPOT instances, KEDA scale-to-zero, scheduled CronJobs, and strict CPU/memory caps. No idle compute. No waste. **Languages by footprint:** Terraform/HCL, Go Template, Shell, Python, Dockerfile, PL/pgSQL. It’s a lot, I know. But that’s kind of the point. The best way I learn is by building systems that behave like production — with real security, real bills, and real incidents to debug. Current status: pursuing my Google Associate Cloud Engineer certification (target July) while iterating on the platform daily. Big takeaway so far: GitOps doesn’t just make deployments safer. It makes you think harder about every change before it ships. Would love to hear from others running similar setups — what’s your approach to secrets management in Git? Or balancing GPU costs with response latency in vLLM? #DevOps #Kubernetes #GitOps #Terraform #GKE #CloudNative #PlatformEngineering #AIInfrastructure #ZeroTrust #CloudComputing

We also provided some feedback to the foundation to help improve AgentForge ;-)

Every AI Agent is built from the same four pillars: a brain that thinks, tools that act, memory that holds context, and instructions that brief the job. AgentSuite offers all four in one place, no coding necessary. Build agent services everyone wants with AgentForge. Sign up for early access → veworld.com