Re: Microservices Agentic Infrastructure

Owen,

I'm copying to the list the summarization Claude.ai did for you for my
previous post.

The post was cleaned up and updated since then:
https://sebxama.blogspot.com/2026/05/microservices-agentic-infrastructure.html

Still an ideas draft, possible implementation of the concepts outlined in:
https://sebxama.blogspot.com/2026/03/applications-of-large-graph-model.html

Refer to the current docs at:
https://github.com/sebxama/sebxama/raw/refs/heads/main/Objective.docx

Thanks,
Sebastian.


On Mon, May 11, 2026, 12:38 AM Owen Ambur <owen.ambur@verizon.net> wrote:

> Sebastian, I prompted Claude.ai to help me make sense of this in relation
> to the compendium of W3C groups <https://stratml.us/docs/W3CCBG_Part2.xml>
> that it created for me.  Here's its response:
>
> This is a rich architectural document with unusually deep resonance across
> the compendium. Let me map it systematically.
>
> *Primary relationships — the architecture's core is essentially built from
> these groups' work:*
>
> *2.9 Knowledge Graphs* is the central nervous system of the whole design
> — the KG functions as the shared blackboard state store, with all agents
> reading and writing to it via RDF Quads events. *2.6 JSON & Linked Data*
> provides that wire format (RDF Quads). *2.23 RDF Streams* maps directly
> to the CRUD event stream pattern: RDF Streams is defined as "a common model
> for producing, transmitting and continuously querying RDF Streams," which
> is precisely what the Message Broker does here. *2.7 Entity
> Reconciliation* maps to the Augmentation layer's "ontology entity
> matching, links/attributes prediction" — the document describes entity
> reconciliation as an automated, event-driven process. *2.8
> Ontology-Lexica* maps to the context alignment and role/behavior modeling
> that Augmentation performs across ontological entities.
>
> *6.7 AI Agents* is the most direct match in the compendium: "develop
> open, interoperable protocols that enable AI agents to discover, identify,
> and collaborate efficiently across the Web." The Syndicated API Gateway
> making agents discoverable as tools for other agents is precisely this. *6.6
> Autonomous Agents* overlaps here too, particularly the multi-agent
> orchestration where Augmentation outputs feed Agent inputs in a pipeline.
>
> *2.11 Hypermedia* maps directly to the HATEOAS Interactions Endpoint —
> HATEOAS is the defining architectural constraint that group works on, and
> the "Dynamic HATEOAS Interactions Endpoint exposing possible executions" is
> a textbook application of it.
>
> *Secondary relationships — significant but one layer removed:*
>
> *6.3 AI Knowledge Representation* covers the declarative context
> modeling, generative grammar for system prompts, and use-case activation
> logic in the Augmentation layer. *6.4 Cognitive AI* addresses "cognitive
> agents using graphs, statistics, rules and graph algorithms to handle
> uncertainty" — the Augmentation layer's context activation and behavior
> inference fits squarely here. *6.2 Machine Learning* covers the Custom
> Embeddings component. *2.19 Shape Expressions* maps to "Schema Patterns
> Signatures" for structured I/O validation — ShEx provides exactly the
> grammar for validating RDF graph shapes used as message contracts. *2.13
> Dataspaces* covers "Pluggable Backends Ingestion/Sync Integrations." *6.13
> Data Synchronization* covers the incremental sync patterns for resource
> ingestion.
>
> *6.21 AI Web Content* is relevant to the API Gateway layer — that group
> develops formats for making web content optimally consumable by AI agents,
> and this document proposes exactly such an endpoint. *6.5 Human Centric
> AI* covers the conversational interaction model and the safety/rights
> implications of agentic systems operating on behalf of users.
>
> *Notable gaps in the compendium this exposes:*
>
> Nothing in the compendium specifically addresses *message broker
> infrastructure for agentic systems* (the Kafka/event-bus layer for AI),
> nor *generative grammar-constrained prompt construction* as a formal
> approach to LLM interaction. The closest is 6.3 AI Knowledge Representation
> but that group's scope is broader and less implementation-focused. This
> architecture could represent a genuine proposal for a new Community Group —
> something like "Agentic Web Infrastructure" — that would sit between 6.7 AI
> Agents (protocols) and 6.6 Autonomous Agents (multi-agent systems) and
> focus specifically on the event-driven microservices substrate that makes
> large-scale agent deployment practical on the web.
>
> Would you like me to draft a StratML Part 1 objective block for it as a
> candidate addition to W3CCBG.xml, along with the corresponding Part 2
> relationship entries linking it to the groups above.
>
> Owen Ambur
> https://www.linkedin.com/in/owenambur/
>
>
> On Sunday, May 10, 2026 at 02:54:43 PM EDT, Sebastian Samaruga <
> ssamarug@gmail.com> wrote:
>
>
> Hello.
>
> Maybe this isn't yet "human readable" (like most of my previous suff). But
> I feel in the mood of sharing everything I thought of in the hope of
> clarifying myself. I don't know if this lists are the right place,
> apologies for the eventual off-topic.
>
> This time is about and appendix of my previous post, regarding the
> inference of Agents "system prompts" using contextualized Generative
> Grammar productions and the guidance of Agents dialog interactions by means
> of context state constrained grammars (possible prompts completions).
>
> It's just a list of "thinking out loud" ideas. Barely fits in the category
> of a To Do list. The attempt is to know if someone agrees with the overall
> concept or to know if this is something already implemented somewhere else.
>
>
> https://sebxama.blogspot.com/2026/05/microservices-agentic-infrastructure..html
>
> Best Regards,
> Sebastián.
>
>