Express YourSelf !

I did not attend this event in-person. I gone through YouTube Playlist https://www.youtube.com/playlist?list=PLcWDDGrTp5AU It has 41 videos

In part 1, let me cover few of them

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1. Made in India. the founders behind API tools

• specmatic
• Beeceptor
• keploy.io
• postman
• bruno
• karate labs

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2. Restoring Trust in AI-Native development

Earlier we used to have Pre-commit hook before agent started coding. 

Now, SDD = spec driven development

Vibe coding was based on prompt

now spec is new source code

Harness engineering

1. Guides gives feedback to agent

2. Sensors for self-correcting loop

3. Executable intent

4. Executable architecture

5. Continuous Governance

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3. Death of API Management

The speaker is founder of "bunny and cloud" a collaborative development tool for humans and AI

https://bunnyandcloud.com/

He explain reasons. 

1. A New practices is emerging: Context engineering

A right context at right time for agent, so it can reason and act reliably. 

2. Agents are taking all management attention

AI GWs and context contracts extend API Management into agent governance. 

3. API Management does not get intent

But agent needs intent

We need to hard code and orchestrate the agents for different workflows. Agent does not think about workflow. 

4. APIs are relegated to the execution layer

Reasoning layer by LLM (Probabilistic) 

context layer by MCP and RAG

Execution layer (deterministic) 

5. The GW shift

Kong is decoupling API GW and AI GW

Portkey AI GW pioneer is acquired by Palo Alto Networks

AI GW enforce policy to every call to LLM, MCP, RAG

6. The tokenomics is replacing APInomics

7. Context Management includes managing APIs

Microcontext. Not DB dump. Agent shall receive smallest truthful slice of bounded context. 

8. From API endpoint Management to capabilities management

Agent Registries

Instead of DX, now we need AX (Agent eXperience) 

Context Management is new API Management

pillar 1: Identity and intent context

pillar 2: Business and domain context

pillar 3: Knowledge and evidence context (RAG) 

pillar 4: Execution and feedback context (MCP)

AI GW examples

1 Portkey AI GW

2 Kong

3 Truefoundry (someone added from the audience) 

The attack surface is different for AI GW. 

36. Skills and MCP

https://www.skills.sh/

API related classes / certifications

https://apimasters.io/