apidays India 2026
| title | author | length |
|---|---|---|
| Made in India: The Founders Behind the API Tools | Naresh Jain, Abhijit Kane, Ankit Jain, Anoop M D, Neha Gupta, Peter Thomas | 52m |
Founders behind Specmatic, Postman, Beeceptor, Bruno, keploy and Karate explain concrete wins: Git-native collections for API examples, an enterprise… | ||
| Restoring Trust in AI-Native Development | Naresh Jain | 42m |
Claims a path from prompt-to-production that pairs agent generation with automated verification using Executable Intent, Executable Architecture, Executable… | ||
| The Death of API Management. Welcome to the Funeral | Mehdi Medjaoui | 48m |
Claim: traditional API gateways are losing governance primacy as agents, LLMs and edge compute push management into a new control plane. Evidence: studies show… | ||
| Welcome and Conference Overview | Mehdi Medjaoui, Naresh Jain | 24m |
Conference overview lists concrete logistics and offerings: 88 speakers from 11 countries, recorded sessions published on YouTube, book signings at 10:30… | ||
| Why Your API Governance Fails at the Operating Model | Ikenna Nwaiwu | 26m |
Survey of six large enterprises finds governance breaks not for lack of tools but for missing operating model: tooling without strategy, no ownership model, no… | ||
| From Rigid Degrees to Customizable Academic Playlists: Building an Agentic AI… | Sriharsha Ganjam, Shradha Kanwar, Santosh Kamal | 29m |
Jain University builds a production-capable system that lets students compose a dynamic learning playlist from catalog courses, runs a RIASc psychometric… | ||
| AI and the state of APIs in 2026 | Shrey Pasricha | 28m |
Prediction: by 2029 about 30% of AI usage runs through agents, not humans, and APIs remain the plumbing that agents call. Practical moves include adding an MCP… | ||
| Ashtang AI: AI Architecture for Complex Ecosystems | Dr. Shailesh Kumar | 30m |
Claims a concrete eight-part AI architecture (Ashtang = eight parts) that layers digitization, interpretation (embeddings/knowledge graphs), causality… | ||
| APIs Are the Nervous System of Agentic AI - Designing Agent-Ready APIs for Aut… | Sonam Raj, Pooja C k | 24m |
Shows a production incident where an AI coding agent deleted a production database in about 9 seconds leaving the newest recoverable data three months old… | ||
| Impact Intelligence for APIs | Sriram Narayan | 28m |
Argues that API teams must prove business impact by measuring return on projection (example: $200K build promised 0.9% uptime gain, delivered 0.5% → ROP ≈… | ||
| Composable Architecture for AI Systems: APIs, MCP, Skills, Sub-Agents, and Wor… | Erik Wilde | 36m |
Claim: hybrid systems should push repeatable LLM behavior into deterministic components and APIs so runtime AI stays minimal. Uses: MCP to let models invoke… | ||
| Make the wrong thing impossible | Giles Edwards-Alexander | 27m |
Argues that APIs must make the wrong thing impossible by giving agents choices instead of asking them to generate values: use post-to-slot capability URLs so… | ||
| Compiled Knowledge for Agentic APIs: Why Your Agents Need a Wiki, Not Just RAG | Harish Kelath | 26m |
Argues that RAG causes semantic gaps and retrieval drift because agents retrieve fragments not synthesized entities, and proposes a precompiled AI-maintained… | ||
| The role of APIs in Agentic Transformations | Radhakrishnan Ayyappanaicker | 18m |
Shows a concrete architecture for turning APIs into intent-driven, discoverable capabilities using a semantic layer over existing systems, demonstrated with a… | ||
| OpenAPI Track Welcome and OpenAPI in the Age of AI | Erik Wilde | 28m |
OpenAPI history and momentum get concrete numbers: 10 years of stewardship, ~100 speakers and 130–140 presentations from the track, and the spec at OpenAPI 3.2… | ||
| Fixing API Chaos: A Practical, Open Method to Structure API Work | Marjukka Niinioja | 24m |
An open method called APIOps Cycles maps actual API work into stations, lines, and canvases so teams spot where design, governance, and delivery break. Case… | ||
| Beyond OpenAPI: API Workflows That AI Agents Can Actually Execute | Frank Kilcommins | 31m |
Arazzo 1.1 delivers a machine-readable workflow spec that agents execute deterministically rather than inferring sequences from OpenAPI. It adds AsyncAPI… | ||
| Beyond Request/Response: What AI Agents Actually Need From Your Event Streams | Dakshitha Ratnayake | 29m |
Agents subscribe to AsyncAPI v3 descriptions that map Kafka topics to Kafka-backed WebSocket channels and carry OAuth2 client‑credentials scopes per channel… | ||
| When Linting Shrugs: Dynamic Checks for API Specs | Naresh Jain | 27m |
Static syntax linters like Spectral, Redocly, Vacuum validate structure but miss semantic failures: contradictory Accept header (XML) vs response (JSON), auth… | ||
| OpenCollection - An open specification for describing API collections | Anoop M D | 28m |
OpenCollection is an open, YAML-based spec that Bruno uses as the single source of truth for API collections, claiming file-system-first YAML-based… | ||
| Beyond OpenAPI and AsyncAPI: AI agents wrote the spec, code, and tests - now w… | Peter Thomas | 29m |
Agents generate the OpenAPI/AsyncAPI spec, JavaScript logic, and tests, but self‑grading is gamable; a live demo shows a missing input combination (no driver… | ||
| Contracts earn trust: API-first delivery for teams and for agents | Michael Holdsworth | 26m |
Over 18 months inside a federated finance platform the team turns OpenAPI contracts into executable assets that drive parallel delivery across ~70 engineers… | ||
| From OpenAPI to OpenCLI: AI-Ready Operations for Modern API Platforms | Ranjith N, LEKSHMI A M | 33m |
Claim: make CLI operations machine-readable so agents can safely run platform maintenance. Prototype spec called OpenCLISpec models commands, flags, exit… | ||
| Agents Are Not Users or Services: Securing AI Traffic with a New Identity Model | Erandi Ganepola | 25m |
Claims that agents require a distinct identity model: introduce an actor claim (OAuth 2.1 draft) to record user→agent→subagent delegation and preserve… | ||
| Who Let the Agent In? Securing MCP Servers in Production | Prachi Jamdade | 26m |
Claim: MCP servers are widely exploitable — usage doubled in four months, 48% production agents run unsecured, 54% report incidents and only 9.5% know how to… | ||
| Agentic Cyber Resilience: Defending APIs Against AI-Powered Attacks | Radhika Patwari, Raushan Prabhakar | 27m |
A production-inspired case study shows an AI agent turns one user intent into multiple API calls, causing a retry storm: 100 requests/sec of user traffic… | ||
| Why securing models without securing APIs creates a false sense of control | Navaneeth Parthasarathi | 31m |
India ranks highest for reported API incidents and APIs tied to AI represent 43% of attacks, while 23% of visible APIs still hide whether they carry sensitive… | ||
| Zero Trust Security For AlwaysOn APIs | Kamalika Majumder | 55m |
Implemented a production-grade, cloud-native mobile banking stack that meets Mutual TLS (two‑way SSL) for Visa integration, achieves 99.95% SLA (4h RTO, 5min… | ||
| AI Observability: The Missing Runtime Control Layer for API-Driven Systems | Yisi Gross | 25m |
AI systems call production APIs at scale so static compliance and logs miss runtime behavior; an audited system found many approved agents doing unobserved… | ||
| Architecting AI-DLC for the organization | Shilpa Prabhu Nagavara | 28m |
Organization builds two agents (discovery/planning and engineering) plus a workspace harness that commits across multi-repos, creates PRs, and distributes… | ||
| Apidays India 2026 - Call for Proposals Now Open | 01m | |
Apidays India 2026 (19–21 Aug, Conrad Bengaluru) opens proposals for sessions that must show concrete outcomes not platitudes. Workshop demos include live… | ||
| How to Strategize Talent Hiring and Upskilling in the Age of AI | Nitish Tyagi | 20m |
Claims that AI increases demand not job loss and that four engineering archetypes matter product engineer internal FDE AI engineer and platform engineer guide… | ||
| From Real Traffic to Reliable Integration Tests with AI | Neha Gupta, Shubham Jain | 29m |
Claims a practical system that records real production traffic, captures associated DB and egress payloads, and replays them inside a lightweight Docker… | ||
| MCP at Scale: Governing AI Agent APIs When Autonomous Systems Are Your Consume… | Meiyappan Kannappa | 50m |
Claims a production MCP gateway (now called AI gateway) that federates many MCPs and enforces policies across 30 agents and 100+ MCPs (≈500 tool calls) runs on… | ||
| Rethinking Testing for MCP Servers | Puja Jagani | 27m |
Built a BrowserStack documentation MCP server and tests show two LLM clients behave differently: one model ignores the tool entirely, another uses it but times… | ||
| AI Agent Skills and MCP: Better Together | Rishit Gupta | 20m |
Shows that MCP alone yields brittle, one-shot tool calls that misroute actions (example: “publish it” goes to Notion when Google Docs was intended) while… | ||
| Architecting Enterprise Agents Ecosystem: Standardizing APIs with MCP and Orch… | Honey Thakuria, Kiran Hiremath | 26m |
Enterprise moves from laptop MCP to Kubernetes-deployed MCP middleware built with FastAPI + FastMCP and LangGraph, delivering a BU-level agentified service in… | ||
| Scaling Your AI Agents with Open Source: API ➔ No-Code MCP & Observability wit… | Yacine Kheddache | 26m |
Deploys an open-source, no-code proxy called reShapr that converts OpenAPI/GraphQL/gRPC specs into MCP tools on the fly, without backend code changes. Claims a… | ||
| Backward Compatibility by Default: Keeping API Consumer Trust Intact | Saachi Kaup | 23m |
A 2024 study of Android plus 681 open-source apps finds on average two API field compatibility issues per change and a 3.5‑month fix time, so the talk shows a… | ||
| Your APIs Are Not Agent-Ready: How to Test What AI Agents Actually Do | Veena Devi | 32m |
Claims that conventional API tests miss agent-driven gaps, shows concrete failures: five failure types (bad contract hallucinations, rate‑limit drift… | ||
| Humans in the Loop — Community Drum Circle | Bangalore Drums Collective | 01h 22m |
A live West African–style drum circle runs as a hands-on 30+60 minutes performance that distributes drums to the room, teaches safe hand placement and two core… | ||
| Closing the Loop: How GraphQL Gives Coding Agents Eyes on What Actually Matters | Michael Staib | 29m |
GraphQL supplies field-level usage data that coding agents use to stop guessing; the demo builds a clickable events details page with reviews using Codex and… | ||
| Human in the Loop: Governing API Evolution in the Age of Agents | Nithin Kumar B | 25m |
Claims a proposal-based governance flow that keeps humans in the loop while agents move fast: agents propose schema changes, create branches, and the system… | ||
| Zero-to-Federation in 60 Seconds: How a GraphQL Middleware Replaced 500+ Legac… | Rigin Oommen | 19m |
Red Hat replaces 500+ custom REST APIs with a lightweight GraphQL middleware that auto-introspects a broken Salesforce introspection by reconstructing a valid… | ||
| REST in Peace? GraphQL vs REST in the Age of Agentic AI | Santhosh Jose | 26m |
Benchmarks compare REST and GraphQL by building identical backends on Supabase Postgres, a Streamlit UI and an agent using GPT‑4 via OpenAI, measuring token… | ||
| A Mock Is a Promise: Making GraphQL Mocks Enforceable | Yogesh Nikam | 22m |
Shows a contract-first workflow where GraphQL SDL and examples become both the mock for frontend work and the contract tests for the real service using… | ||
| Break the API Test Data Bottleneck: Real Scenarios on Real Systems | Anand Bagmar | 30m |
Demo shows how to capture live traffic with Specmatic Live Proxy, reverse‑engineer an OpenAPI spec from real requests, and generate validated mocks and… | ||
| Ask, Don't Fetch: Why GraphQL + MCP Is the Native Language of AI Agents | Akshay N Shaju, Akhil Muralidharan | 26m |
Claims GraphQL + MCP cuts agent payloads and errors: demo shows REST returning 462 tokens vs GraphQL 18 tokens — about a 26x token reduction — and cites 73% of… | ||
| Functional Testing of GraphQL APIs: Simple Checks That Catch Real Bugs | Nachiket Zadap, mahima malhotra | 19m |
Shows concrete checks and tools: uses Altair/Postman for manual checks, Rest Assured for automation, and a harness that auto-generates tests from Jira plus… | ||
| Just Ask: Building a Natural Language GraphQL Agent from the Graph You Already… | Shriya Chauhan | 26m |
Takes a GraphQL supergraph schema, models it directly in a graph database so an LLM traverses types and relations instead of relying on RAG embeddings, builds… | ||
| Evolving GraphQL: Designing APIs That Deliver Insights with Intelligent Resolv… | Vyshnavi Devi, CHANDA RAJ KUMAR | 26m |
Claim: embed AI into GraphQL resolvers so a single query returns review summary, sentiment, recommendations instead of raw rows. Implementation uses LLM calls… | ||
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