Conf42 DevOps 2026
2026-01-20..2026-01-21
-- online --
who it's for
platform engineers, site reliability engineers, devops engineers, security engineers, machine learning engineers, cloud engineers, engineering managers
topics
agentic AI CI/CD pipelines; autonomous time series forecasting; multimodal conversational agent pipelines; LLM-driven multi-cloud provisioning; self-healing Tekton and ArgoCD pipelines; infrastructure as code for SD-WAN and zero trust; predictive observability and risk scoring; Model Context Protocol for observability; template-driven prompt engineering for streaming finance; AI agents for incident response and triage; DevSecOps for AI-era CI/CD pipelines; GPU performance validation in CI/CD
| title | author | length |
|---|---|---|
| Building Enterprise-ready CI/CD Pipelines using Agentic AI | Chinmay Gaikwad | 14m |
Claims enterprise pipelines must be scalable secure and compliant with 80% of SDLC failures happening after code generation not during it; proposes a hybrid… | ||
| AgentCast: Autonomous AI-Driven Time Series Forecasting | Premanand Tiwari | 17m |
AgentCast is a deployed autonomous forecasting agent that ingests streaming or batch time series, runs automated model selection and hyperparameter tuning… | ||
| AI-Driven Workflow Orchestration at Enterprise Scale | Pallab Haldar | 25m |
A concrete system is described: ingestion of build logs test results pipeline metrics and PR metadata into a model-ready telemetry layer, feature engineering… | ||
| From Demo to Deployment: Multimodal Conversational Agents | Gobu Natarajan | 19m |
Claims a field study of 250 households showing a 30% improvement in task completion and natural multimodal adoption within three weeks, plus a tested… | ||
| LLM-Driven Multi-Cloud Infrastructure for 2026 and Beyond | Praneeth Patil | 11m |
Claims an autonomous agent actually provisions and reconciles multi‑cloud resources end to end, using a retrieval layer with domain‑specific vectors and… | ||
| AI Agents for Incidents: The Good, the Bad, and the Ugly | Vladyslav Budichenko | 19m |
Claims include 62% of orgs running AI agent pilots, concrete integrations with PagerDuty Honeycomb New Relic Grafana Elasticsearch Slack Jira and GitHub, and… | ||
| GenOps: A Governance-Driven Model for Safe | Neeraj Kumar Singh Beshane | 20m |
GenOps claims a concrete stack: contextual prompt injection that pulls build logs and incidents, an OPA-enforced blast radius score gating merges, SLO-driven… | ||
| Zero-Downtime by Design: High-Availability Migration Patterns | Kalyan Inturi | 19m |
Claims a full legacy gateway migration to Kong completes with zero customer-facing incidents using an eight-phase Terraform-driven rollout. Validation uses a… | ||
| Scaling Microservices Development | Ravi Babu Dasari | 19m |
Claims a working STDP (Scalable Development Deployment Platform) that uses Kubernetes, Helm, Argo CD and dynamic ingress to create a dedicated namespace per… | ||
| Scaling APIs to Billions: Extreme-Scale Reliability | Nikhil Kokal | 18m |
Claims concrete wins and concrete tools: CDN edge caches serve stale responses with background refresh to guarantee sub-50ms edge responses and reduce origin… | ||
| From Lift-and-Shift to K8s: A Two-Phase DevOps Modernization | Harris Baskaran | 24m |
A concrete case study of a legacy ticketing monolith that handles 100,000 tickets every day describes a measured two‑phase move: lift and shift to Google… | ||
| Infrastructure as Code for Enterprise: SD-WAN and Zero Trust | Imran Abdul | 26m |
Claims of real deployments: a 500+ site estate moves from days to 15 minutes for global changes with a 95% success rate. Implementation uses Cisco SD‑WAN… | ||
| Automating Continuous Compliance for Scalable SaaS Delivery | Pooja Ranjan | 29m |
Claims an automated compliance pipeline across the SDLC that ties feature planning to deployment, tests and monitoring using Kubernetes, Argo, GitLab/Bamboo… | ||
| Privacy by Design for DevOps | Vivek Chittireddy | 08m |
More than 140 countries have privacy laws and the average breach cost hit about $5M in 2024 so privacy is nonnegotiable; researchers reidentify 87% from zip… | ||
| Cloud-Driven Compliance for Scalable Provider Network Governance | Sai Hanumara | 05m |
Cloud native architecture with DevOps and analytics claims to convert compliance into executable governance: real-time validation across pipelines, parallel… | ||
| The AI Trust Triad for DevOps | Saideepak Kandibanda | 12m |
Claims concrete operational steps: use reinforcement learning for portfolio reallocation, automated circuit breakers and routing optimization while enforcing… | ||
| Template-Driven Prompt Engineering for Streaming Financial Events | Tina Kanth | 23m |
Claims that template libraries plus versioned prompts and policy rules turn high throughput financial streams into reliable, auditable automations. Uses a… | ||
| Designing a Read-Intensive Architecture for Information Systems | Ivan Sinitsin | 15m |
Claims concrete limits and fixes: Kubernetes microservices hit tail latency past ~1,000 nodes and a hostile example is 1,000 app ports × 100 cache nodes =… | ||
| Observing Agentic/MCP Traffic & Keeping Costs Low | Michael Levan | 08m |
Installs and configures agent gateway on Kubernetes, creates three gateways and backend CRDs that route to an Anthropic/Claude model by rewriting paths, then… | ||
| AI Subscription Platforms in DevOps: Transforming Models & Ops | Chandan Kumar | 11m |
Claims concrete operational requirements and trade offs: a subscription AI platform rests on four fundamentals: cloud infrastructure, model management, data… | ||
| Pipelines for Clinical AI: Real-Time Sepsis Detection | Swapna Chimanchodkar | 18m |
Claims a pipeline that ingests real‑time vitals and labs, runs feature engineering and inference with Apache Kafka and geo‑redundant microservices, and targets… | ||
| Operationalizing Predictive Analytics in Education | Venkatesan Kandavelu | 15m |
Claims a working MLOps pipeline that moves predictive student risk models from notebooks to production with reproducibility, monitoring and governance and… | ||
| AI Driven Vectorized Motor Health Monitoring | Avinash Sista | 20m |
Claims a practical pipeline: multi-layer diagnostics tying design conformance, input current spectra, output reactions and aging trends into one decision loop… | ||
| Deterministic, Adaptive, and Sustainable Robotics | Rasmi Ranjan Nayak | 15m |
Claims deterministic control and adaptive AI coexist and backs it with concrete engineering: a Real‑Time Embedded AI Framework (RE AIF) using C++ core with… | ||
| Human–AI Collab for Reliable Automotive Control Systems | Alagar Raja Govindasa | 08m |
Claims a human-centered collaborative control stack that reduces critical incidents by measurable amounts using multi-sensor fusion (camera, radar, LIDAR) plus… | ||
| Building Robust GPU Performance Validation into CI/CD Pipelines | Mohit Gupta | 07m |
Claims a concrete CI/CD implementation that captures GPU counters, classifies workloads, and enforces regression gates, not a high‑level pep talk. Benchmarks… | ||
| Context Engineering for DevOps: Turn Your Infra into a Graph | Roxane Fischer | 14m |
Claims that a live knowledge graph reconciles cloud state, Terraform code and remote state to detect drift and shadow IT, built by any shift and implemented… | ||
| Self-Healing CI/CD Pipelines with Tekton and ArgoCD | Srinivas Pagadala Sekar | 51m |
Claims concrete results: a 10x drop in meantime to recovery from 20 minutes to 90 seconds, empirical claim that ~70% of incidents stem from deployment… | ||
| O11y as Platform Contract: Instrumentation and CI/CD-Enforced SLOs | Sumit Kaul | 36m |
Claims a working platform that delivers zero-code OpenTelemetry agents via base container images and sidecars, with CI/CD contract gating to enforce SLOs… | ||
| Predictive Observability: Prevent Supply Chain Outages | Raghu Bhupatiraju | 16m |
Claims concrete gains: an event-driven pipeline using Apache Kafka or AWS Kinesis streams feeds ML prediction services that run offline from production… | ||
| AI-Powered Reliability: Stop Fighting Fires, Prevent Them | Aravind Sekar | 27m |
Shows an operational path from streaming telemetry to layered models with near realtime inference and continuous validation and gives concrete outcomes: 92%… | ||
| Building Queryable O11y with the Model Context Protocol | Pronnoy Goswami | 24m |
Claims a concrete pipeline: context enrichment + a queryable MCP server that sits between telemetry stores (InfluxDB/Prometheus) and LLMs (Anthropic, OpenAI)… | ||
| Security and Accessibility can be friends! | Gian Wild | 25m |
An accessibility consultancy reports concrete results: tools called OS Player and Ozart and an automated accessibility testing tool built and used for 15… | ||
| IP Detection at Scale: CI/CD Meets Cloud-Native Enforcement | Kanika Gupta | 15m |
A production pipeline that ingests millions of listings per day and uses multimodal embeddings with ANN via AWS OpenSearch performs subsecond P99 lookups at… | ||
| Green Checkmarks, Red Flags: What CI/CD Can't Catch | Tilda Udufo | 26m |
A March 29th 2024 incident shows CI/CD reporting green while a subtle performance anomaly exposes the Ex UTS backdoor planted by a trusted account named Jatan… | ||
| Building Security Into Your DevOps Pipeline | Gurdeep Gill | 26m |
Claims a concrete integration: next‑generation firewalls wired into CI/CD with policy as code in git, identity‑anchored rules, and five coordinated controls… | ||
| Defender XDR in the Real World: Measurable Risk Reduction | Nikolay Milyaev | 19m |
Claims a four‑stage pipeline that cuts MTR by turning noisy alerts into correlated incidents then automating enrichment, adding AI summaries, and measuring… | ||
| Zero-Trust Security: Trust Verified Across 5G Pipelines | Vijayakumar Venganti | 09m |
Claims concrete outcomes: adoption stats (over 60% now, 81% planned), a private 5G pen test showing networks breachable (compromised in under 30 minutes), and… | ||
| Guardrails First: Designing AI Agents for Security Operations | Neal Iyer | 17m |
A concrete framework claims to make AI agents safe and auditable by centering four pillars: organizational context, policy constraints, full audit trails, and… | ||
| DevSecOps Playbook for AI-Era CI/CD Pipelines | Bhaskar Sawant | 12m |
CI/CD pipelines now ingest ~40% AI‑generated enterprise code and 78% of organizations report supply‑chain incidents, so the playbook names concrete defenses… | ||
| Building Reliable Human-AI Warehouse Operations at Scale | Vishnu Prasad | 27m |
Claims concrete outcomes: uses digital twin simulation plus live IoT sensors: temperature motion weight RPM feeding observability pipelines to run detection →… | ||
| The Multi‑Workload Challenge: Predictable Failover | Akshay Pratinav | 14m |
Builds a declarative DR platform that executes a four‑stage DR procedure YAML (pre‑scale, promote DB, dial traffic, reset) and runs a state machine that posts… | ||
| How AI is redefining SRE and Customer Experience | Spiros Economakis | 16m |
Claims that embedding AI into the production workflow creates persistent system memory that links code config deployments logs and telemetry into… | ||
| Keeping High-Traffic Systems Running When Things Go Wrong | Justice Eziefule | 19m |
Specifics: Postgres queries slow from ~15ms to ~150ms, one user action triggering six internal calls turns 100ms pieces into 600ms end-to-end, P99 spikes to… | ||
| AI Threat Screening for 5G UPF with Automated Risk Mitigation | Binu Govindan | 11m |
Claims an AI-driven multi-risk scoring system that scores each UPF session from five signals: behavioral, geographic, temporal, volumetric and historical… | ||
| From Zero to a DevSecOps Hero: Extending Microsoft Sentinel | Gareth Emslie | 30m |
Claims a working pipeline that pulls Dinatrace attacks API into Microsoft Sentinel using a codeless CCF connector and a DCR v2 ingestion path, shows that… | ||
| Gen AI Workflows for Climate Risk Modeling in Food Systems | Poshan Kumar | 09m |
ERA5 provides 31 km global analysis since 1940 and Sentinel‑2 supplies 10–20 m multispectral revisits; CMIP6 defines SSP futures. GraphCast GNN claims >90%… | ||
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