IBM Developer @IBMDeveloper
Join our community to explore agentic AI, data science & cloud tech through tutorials, challenges & expert insights. Build skills, learn & grow with us. ibm.biz/BdeeE9 Global Joined September 2008-
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everyone's building simple agents meanwhile IBM is building robust enterprise agents in production, and it's open-source they just dropped a blog on HF breaking down how to go beyond LLMs & agents: structured reasoning, tool use, and more to scale AI across enterprise
Building with Terraform or Vault? 🏗️ Join @HashiCorp experts for live exam prep sessions designed to help you assess your knowledge, brush up on key concepts, and prepare for certification. Sign up today → ibm.co/6045EMUo5
Start building with IBM Bob👇 ibm.co/6018EMiGC
New to IBM Bob? This quickstart helps you get started with: 🧰 Environment setup 📂 Codebase exploration 🛠️ Feature development 🔄 Testing changes
Artificial Analysis and IBM Research are launching ITBench-AA, the first in a new series of benchmarks evaluating models on agentic enterprise IT tasks, starting with Site Reliability Engineering tasks where frontier models score below 50% ITBench-AA’s SRE tasks benchmark model performance on Kubernetes incident response, where models must diagnose live systems by reading logs, tracing dependencies, and identifying root-cause entities across complex infrastructure. The underlying ITBench dataset has been developed by @IBM's Software Innovation Lab, leveraging IBM’s deep expertise in enterprise IT operations Artificial Analysis has worked closely with IBM over the last 6 months to develop a implementation of the dataset for frontier AI evaluation, beginning with Site Reliability Engineering (SRE) and expanding to Financial Operations (FinOps) and Chief Information Security Officer (CISO) tasks over time ITBench-AA SRE overview: ➤ 59 SRE tasks in total: 40 public tasks and 19 brand new, held-out tasks ➤ Each task provides a Kubernetes incident snapshot containing alerts, events, traces, metrics, logs, and application topology. The model must identify the minimal set of independent root-cause Kubernetes entities responsible for the incident ➤ Faults span typical SRE failure modes including infrastructure, service, application, and chaos-injected incidents, such as resource quota exhaustion, rollout failures, connection pool exhaustion, and network partitions Methodology details: ➤ Agentic harness: each task is solved by the model running in our open-source Stirrup reference harness, with shell access to a sandboxed file system containing the relevant logs and snapshots. 100-turn cap per task, 3 repeats per task ➤ Models submit a list of root-cause entities (Kubernetes Deployments, Services, Pods, etc.) they believe caused the incident. Each submission is compared against a ground-truth set of root causes provided by IBM Research ➤ Scoring uses average precision at full recall: if a model misses any of the ground-truth root causes, it scores 0.0 for that repeat. If it identifies all of them, it is awarded a score equal to its precision - the share of its submitted entities that are actual root causes, i.e. true positives / (true positives + false positives). The headline score is the average across 59 tasks × 3 repeats. ➤ The harness (Stirrup) is held constant across all evaluated models, allowing an apples-to-apples comparison between models. Key findings: ➤ Claude Opus 4.7 (Adaptive Reasoning, Max Effort) leads at 47%, followed by GPT-5.5 (xhigh) at 46% and Qwen3.7 Max at 42% ➤ All frontier models score below 50%, making ITBench-AA SRE one of the least saturated agentic benchmarks in our suite. For context, frontier models score considerably higher on Terminal-Bench ➤ Turn counts vary nearly 3x and longer trajectories do not translate to higher accuracy. GPT-5.5 (xhigh) averages 31 turns per task at 46%, while Gemini 3.1 Pro Preview averages 83 turns at 30%. Models that over-investigate tend to surface upstream fault-injection mechanisms or co-occurring symptoms as false positives ➤ GLM-5.1 (Reasoning) leads open weights models at 40%, effectively tied with Gemini 3.5 Flash (high). DeepSeek V4 Pro (Reasoning, Max Effort) follows at 38%, with Gemma 4 31B (Reasoning) at 37%, ahead of Gemini 3.1 Pro Preview at 30%
Another IDE? There are already too many of those. Nicholas Renotte gives an honest review of IBM Bob and what it actually feels like to build with it.👇
Top 5 Horror Movies 1. “The PM vibe-coded a feature and it needs to ship today.” 2. +123918, -1012 3. “I pushed the wrong migration to production.” 4. “What’s staging?” 5. "We don't have an on-call rotation; it hasn't come up."
Tap the bulb to meet your dev partner👇 💡
Writing the playbook is only part of the work. The real overhead is: ⚙️ handlers 👥 roles 📄 templates 🔄 conventions 📚 docs @alexsotob shows how Bob can generate and organize that scaffolding automatically: ibm.biz/~t0WMP3FIA
Expose a Legacy Java App as an MCP Server Using a Kubernetes Sidecar Expose a Legacy Java App as an MCP Server Using a Kubernetes Sidecar: 🏗️: No changes on the legacy app 🛳️: Using Containers and Kubernetes ♻️: No side effects youtube.com/watch?v=3llzsh…
Find out how Docling + OpenRAG make videos searchable, so you can stop scrubbing through hour-long recordings for one insight. 🎥 Full video: ibm.co/6046Ez2Ij 💻 GitHub repo: ibm.co/6047Ez2Id
Turn YouTube videos, podcasts, and conference talks into searchable knowledge. @SonicDMG shows how to use Docling + OpenRAG to: 🎤 extract transcripts + timestamps ⚙️ build an AI-ready RAG pipeline 🔎 search exact moments from long-form videos
@antoine_bit @lablabai Great work thanks for sharing!
@IBM Try IBM Bob for free today: ibm.biz/~23ugoLzU8
I get suspicious when trim().toUpperCase() turns into an agent. This walkthrough shows where MCP tools fit in a LangChain4j workflow: one Quarkus MCP server, one workflow app, /topology, and tests against Ollama. buff.ly/OiFml9q #langchain4j #java #quarkus #ollama
@sarbjeetjohal @IBM Thanks for sharing!
As context windows continue to grow, where does RAG still fit into modern AI workflows? @MartinRTP explores how RAG and long-context models compare when building AI apps and agents, and why grounding, latency, and accuracy still matter in practice: ibm.biz/~kicuIECgi
javinpaul @javinpaul
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