GCCs Shift to Agent-Led Model with Java-Powered Agentic Assets

The integration platform you bought in 2022 was designed for a world where every API had a contract you could trust. That world is shrinking. Internal tools change weekly, vendor schemas drift, and half the systems your team touches now have an LLM in the middle making the response shape probabilistic. iPaaS pipelines weren't built to reason about that — they were built to move JSON from A to B. AI agents aren't replacing integration platforms. They're absorbing the parts of integration that always required human judgment: mapping, exception handling, deciding what 'the same record' means across systems. Where the line actually falls — and where teams are getting it wrong: https://lnkd.in/e8-6vMtR

I thought building a voice agent would be the hard part. Three days in, I realised I hadn't even reached the hard part yet. The integration was done. Vapi was connected. n8n was triggering perfectly. Everything worked. Then I made the first real test call. I paused mid sentence to think. The agent panicked. I asked a question it was not expecting. It gave a generic response and lost the thread entirely. I said nothing for five seconds. Silence. Then an awkward recovery that no real customer would sit through. The technology was fine. The conversation was broken. I spent the next week not touching the workflow at all. Just writing. Mapping every scenario. Every objection. Every awkward pause. Every question a confused or frustrated caller might ask. I wrote responses for situations I hoped would never happen, because they always do. By the end, the conversation design document was longer than any technical documentation I had written for the project. Building the agent took a day. Building the conversation took a week. If you are planning to build a voice agent then budget more time for the dialogue than the development. What has surprised you most about building with AI tools? #VoiceAI #VAPI #n8n #AIAutomation #BuildInPublic

Hot take: Your API-first platform strategy is aging fast. I published a piece on something I've been analysing for a while: 𝗔𝗣𝗜-𝗙𝗶𝗿𝘀𝘁 𝘁𝗼 𝗠𝗖𝗣-𝗙𝗶𝗿𝘀𝘁: 𝗪𝗵𝘆 𝗜𝗻𝘁𝗲𝗴𝗿𝗮𝘁𝗶𝗼𝗻 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀 𝗠𝘂𝘀𝘁 𝗘𝘃𝗼𝗹𝘃𝗲 𝗼𝗿 𝗦𝘂𝗻𝘀𝗲𝘁 The core argument: For decades, integration platforms assumed the primary consumer was a human developer. That assumption is now breaking. AI agents don't read your Swagger docs. They don't care about your beautiful developer portal or your 12-language SDK. They need semantic context — not just "here's an endpoint," but "here's what this system can DO, and when you'd want to do it." That's exactly what the Model Context Protocol (MCP) enables. And it changes everything about where competitive moats come from. Key shifts I unpack in the article: → Why REST APIs are being demoted from "the interface" to "the engine room" → Why the real moat is now the canonical semantic model, not developer adoption → How to layer MCP on top of your existing API surface without ripping anything out → Why the window to get ahead of this is narrow and closing I've seen this pattern before. When cloud-native displaced on-premise integration, early movers captured disproportionate value. This shift has the same dynamics. The question isn't whether it's real. It's which side you're building for. 👇 Full article below. Would love to hear from folks in the integration / platform / AI agent space: https://lnkd.in/gSth523S #MCP #APIFirst #EnterpriseIntegration #AIAgents #ProductStrategy #PlatformStrategy #DeveloperPlatforms

How do we achieve seamless API integration in today's complex environments? 🤔 Navigating the myriad of APIs in modern architectures can be a daunting task. Whether connecting microservices or third-party services, API integration reshapes how systems communicate. Here are essential considerations: 🚀 **Scalability**: Ensure your API can handle load demands. Leverage tools like Kubernetes for automatic scaling and load balancing. 💡 **Security**: API security is paramount. Employ OAuth 2.0, API gateways, and encryption standards to secure your endpoints. 🔧 **Compliance & Governance**: Adopt standardized protocols such as OpenAPI and ensure alignment with data regulations like GDPR and CCPA. ⏩ **Automation**: Automate testing and monitoring of APIs with CI/CD pipelines to improve deployment speed and reliability. APIs act as the lifeline of software systems, and mastering their integration is crucial for streamlined operations and innovation. What strategies have you found pivotal in overcoming API integration challenges in your projects? 👉 To explore more about cutting-edge API solutions, visit our AI agent at: https://webcol.net #APIIntegration #CloudInfrastructure #SoftwareEngineering #TechInnovation

Agents are becoming persistent infrastructure. That is the shift long-horizon agents like Kimi K2.6 are exposing. We went from scripts to services to containers to functions. Now agents are creating categories we do not yet have good names for: agent runtime, agent gateway, agent identity provider, agent mesh. The API gateway pattern has to evolve from endpoints and verbs to goals and workflows. I shared more with VentureBeat here: https://go.f5.net/gwniu4s7

For ambitious learners and career-switchers, identifying valuable expired domains can be a lucrative venture. By using a daily scan and custom API scripts, you can filter through thousands of domains and find hidden gems. A systematic process can help you stay ahead of competitors and build a digital asset portfolio. Read more at https://lnkd.in/eE6h7Qe2

Recently, I've been working on a new project at Gadget called Gizmo Support Dashboard. Gizmo Support Dashboard is an internal tool I built to streamline how we handle support tickets at Gadget. The issue was that accurately answering tickets with AI agents requires codebase context, which previously meant the support engineer had to keep the entire codebase open locally while their preferred agent was running. Gizmo Support Dashboard solves this with a web dashboard that ingests incoming tickets via webhooks. For each ticket, it spins up an isolated E2B sandbox containing the codebase, with the Anthropic Claude Agent SDK running inside. The agent drafts a response and streams it back to the dashboard in real time. Because every ticket runs in its own sandbox, the system is fully asynchronous and can process any number of tickets in parallel. The agent also connects to our internal admin MCP server, enabling it to investigate both user-specific issues and broader platform problems on its own. The result is faster, more accurate first-draft responses with zero context-switching overhead for the support team.

Evaluating enterprise document extraction shouldn’t require weeks of NDAs and pilot setups. We’ve added our AutoFill Engine to RapidAPI to give engineering teams a faster way to test our document-to-JSON pipeline. Instead of forcing a heavy proof-of-concept from day one, we’ve formalized a simple two-step validation process: • Visual: Drop a sample CMR or invoice into our public UI to see how unstructured data is mapped to specific business fields. • Technical: Run a quick test via RapidAPI (POST /recognize) to evaluate the exact JSON output architecture directly in your code or Postman. No mandatory discovery calls required to see if the logic works for your documents. When it comes to actual production, the approach remains strictly secure. The engine is deployed 100% self-hosted (in your VPC or on-premise) to ensure complete data sovereignty and compliance. Evaluate the API here: https://lnkd.in/g3rqBNAi Try the demo: https://lnkd.in/d89XdrYz #LogisticsTech #API #IDP

Just drop a sample document into the demo or hit the API endpoint to see the exact JSON output for yourself

We did another AI exercise with our one of our services. There is still complication with prompts that we need to reexplain context within the discussions - infra, db, provisioning etc. We still need to hold agent hand to complete task even if its something obvious. We wanted a centralised way to work on specific epic and make it e2e from start to end. That made us think we are probably underutilising skills for that. Thats why we created this set of skills for specific, where Claude code can help us finish any task within that folder. So far we added these skills: auth - how our auth works code-coverage - defines our code coverage, quality gates db-migrations - how to write correct migrations, ensuring idempotency db-users - what needs to be done to add new db user, grants docker - helps with local docker config docs - writing documentation, dont add long comment, docs naming standards infrastructure - infra docs, how to add new service, values load testing - setup, how to add new load test, smoke load tests logging - standards, logging headers, traces monitoring - metrics, dashboards, how to setup new dashboard provisioning - deployment of services, pipelines run-tests - how to run tests locally, unit + e2e, db seeding So far it definitely improved outputs as some of skills are interconnected. Lets see how we can scale those, since some of them differs per each service.