DEV Community: Christoph Grimm

Improve your Prompt Engineering using Mermaid Diagrams

Christoph Grimm — Tue, 02 Dec 2025 13:18:00 +0000

Summary

You probably have seen a lot of Mermaid Diagrams already without even realizing that what is powering them is a formal description language which is very well understood by LLMs.

Mermaid syntax is very good for describing formal, structural, or temporal information like "A happens, then B, and if X, then C".
It forces clarity and guarantees that the visualization precisely reflects the text definition which makes it a perfect choice for a prompt as code approach.

Complementing your text with additional Mermaid code in your prompt will provide the LLM so called "Dual-Path Reinforcement":

Text provides the context, intent, nuance, and soft constraints.
- It explains the goal of the sequence and the purpose of the participants,
- clarifies ambiguities present in the structural diagram
- and guides the overall interpretation.
Complementary Mermaid Code provides the strict, non-negotiable logic to ground the text.
- It explicitly defines the flow, participants, timing, and conditions (loops, branches) in a way that is less prone to misinterpretation
- and confirms the exact, step-by-step logic, preventing the LLM from "hallucinating" or misinterpreting the flow based on ambiguous human language.

Once a diagram is defined, you as the prompt engineer will simply refer to the visualization, removing the need to re-read the verbose Mermaid code for logical flow. This visual clarity is a significant advantage over other prompt as code approaches like JSON or TypeScript.

Why Mermaid Syntax Helps an LLM

For a Large Language Model (LLM) Mermaid syntax offers several critical advantages when processing structured information:

Natural language descriptions of complex systems ("A uses B, which calls C unless D is present") are inherently ambiguous. the LLM needs to interpret the context and resolve linguistic relationships.
Mermaid syntax is a Domain-Specific Language (DSL) that is highly structured and unambiguous. A --> B means A connects to B. This structure immediately tells the LLM the entities and the directed relationship between them, converting a natural languageinterpretation task into a simple, efficient parsing task.
Mermaid allows the LLMto map the text directly to a well-defined conceptual structure (a graph, a tree, a timeline). This is analogous to how a human can immediately internalize a flowchart versus reading a dense text description.

Example: Describing a Purchase Order Process

To demonstrate the efficiency and structural clarity of adding complementary Mermaid syntax to your LLM prompt, we will analyze a common scenario: a user purchasing an item in an e-commerce system, which involves interaction between three services - Frontend, OrderService, and InventoryService:

User clicks 'Buy' on the Frontend.
Frontend sends a request to the OrderService to initiate the order.
OrderService checks the stock by asking the InventoryService.
InventoryService returns the stock level.
If stock is available, OrderService deducts the stock from InventoryService.
OrderService confirms the order back to the Frontend.
Frontend shows a success message.

Textual Representation

* The purchase process begins when the Frontend sends a request to the OrderService, specifying the Product ID and the desired quantity.
* The OrderService then takes on the responsibility of verifying inventory. It issues a call to the InventoryService to check the current stock levels for that specific product ID.
* After this check, the InventoryService responds back to the OrderService with the current quantity available.
* Importantly, the process includes a critical conditional branch: 
  * If the stock is available, the OrderService sends another message back to the InventoryService to officially deduct the purchased quantity. 
  * Following this, the OrderService sends an Order Confirmation, including the new Order ID, back to the Frontend.
  * Conversely, if the stock is unavailable during the initial check, the OrderService terminates that branch and sends an immediate 'Error: Out of Stock' message back to the Frontend.
* Regardless of the outcome of the order, the final step involves the Frontend internally displaying an appropriate success or failure message to the user.

Complementary Mermaid Sequence Diagram (DSL)

What the LLM actually sees is not the above image but this Mermaid code:

sequenceDiagram
    participant F as Frontend
    participant O as OrderService
    participant I as InventoryService

    F->>O: Initiate Purchase (Product ID, Qty)
    O->>I: Check Stock (Product ID)
    I-->>O: Stock Available (Qty)
    alt Stock Available
        O->>I: Deduct Stock (Qty)
        O-->>F: Order Confirmation (ID)
    else Stock Unavailable
        O-->>F: Error: Out of Stock
    end
    F->>F: Display Success Message

The combined input compensates for the core weaknesses of the LLM:

Ambiguity in Text: Mermaid provides the strict, non-negotiable logic to ground the text.
Hallucination/Creativity: Mermaid forces the model into a rigid structure (like code), constraining its creative freedom to invent steps that aren't there.
Losing Track in Long Context: The diagram acts as an easy-to-parse summary. The model can cross-reference the text against the visual logic flow, improving its long-term attention to the process.

Place the diagram first and the textual description afterwards!

For maximum re-enforcement and clarity in a system prompt, it is generally considered more effective to place the diagram code block (the visual summary) first, followed by the detailed textual rules.

Visual Primacy: The diagram provides an immediate, unambiguous mental model of the entire decision tree. The LLM sees the mandatory flow/sequence before getting lost in the details of the text.
Text as Annotation: When the diagram is first, the subsequent text is processed as annotations and constraints for the visual structure. This solidifies the "what to do" (the path) with the "how to do it" (the rules).
Hierarchy:** It establishes a clear hierarchy: The flowchart/sequence diagram/etc. is the protocol definition, and the bullet points are the protocol details.

Appendix: Mermaid Diagram Types you should consider for your Prompts

Diagram Type	Suitable For Knowledge Representation Aspect	Example Use Case in Code/System Analysis	Key Benefit for the LLM
Flowchart (`graph TD`, `LR`, etc.)	Temporal Flow, Process Logic, Decision Trees.	Mapping the execution path of a complex function, describing a deployment pipeline, or outlining a user sign-up workflow.	Easily extracts process order and conditional logic (e.g., if statement paths) due to explicit node-to-node connections.
Sequence Diagram (`sequenceDiagram`)	Temporal Interaction, API Calls, Message Passing.	Documenting the step-by-step interaction between microservices, a client-server authentication handshake, or the order of event emission.	Clearly defines the order of events over time and the participating entities (lifelines), which is excellent for tracing bugs.
Class Diagram (`classDiagram`)	Structure, Hierarchy, Relationships between Entities (OOP).	Defining the structure of a new module, documenting inheritance, or showing the composition of objects within a codebase.	Unambiguously captures Object-Oriented Programming (OOP) concepts like inheritance, composition, and public/private methods.
Entity Relationship Diagram (ERD) (`erDiagram`)	Data Structure, Relationship between Data Models.	Describing the schema of a database, defining the relationship between data entities (e.g., User, Order, Product) in a system.	Provides a structured map of data entities and their cardinality (one-to-many, etc.), essential for data-driven applications.
State Diagram (`stateDiagram-v2`)	State Transitions, Finite State Machines.	Documenting the lifecycle of an object (e.g., Order status: Draft -> Pending -> Shipped -> Delivered) or a UI component's behavior.	Offers clear logic on valid transitions and the events that trigger them, perfect for analyzing complex object lifecycles.

Note that this article is NOT the single response of a prompt that I gave to an LLM.

It was written by combining and structuring aspects of various interactions I had with Gemini about using Mermaids in prompting, defining initial context in GEMINI.md, and also about guard railing an LLM in the MCP Service I am working on.

My motivation to share this approach simply is because I think it is something genuine and new and might help you in your daily work with LLMs.

Enhance Model Context Protocol with Aspects from Common Object Request Broker Architecture (CORBA)

Christoph Grimm — Fri, 31 Oct 2025 09:10:45 +0000

Quick Note: Readers interested solely in the technical specification of the proposed Enhanced MCP / CORBA-NG Agent Object Protocol can jump directly to the actual spec below.

Co-Author's Note

It is often said that AI can only rearrange existing knowledge, but cannot create new. This statement ignores the fact that science never works in isolation, but in most cases connects existing knowledge with new ideas. The entire process of citation and even the invention of hypertext served to link scientific findings together. The question is not whether humans are better than AI or AI is better than humans, but what they can achieve together.

The following is the result of combining unconventional human thinking with the vast information pool of AI and demonstrates the potential for jointly creating new knowledge.

The idea for this article arose from the fact that I still can't accept that CORBA is long gone. This, and the fact that Halloween was just around the corner, prompted me to at least revive the basic ideas with the help of AI – as an intellectual Frankenstein, so to speak.
My initial prompt was only a question born out of curiosity.
I asked the AI: What would it theoretically mean if we were to further develop MCP so that it communicates via CORBA?
The answer I got was what I almost expected - the usual detailed comparison between the legacy CORBA and the "more modern" REST approach MCP is using.

My response, being almost sixty years old, was:
Just because CORBA is old does not mean it is bad. I suggested that maybe it was just too complex for humans to handle it.
That, however, shouldn't matter too much to an AI. Therefore, I asked it to choose the approach that gives it the greatest advantage.

From then on, it took the question seriously and came to the conclusion that favoring CORBA would indeed have some advantages.
Things really got interesting when I asked how it would move from CORBA to some "Next Generation CORBA", tailor-made to its own MCP-specific needs.
I expected some minor modifications, but what it came up with on its own was a major overhaul of the design with a completely new technology stack, as you can see for yourself further down.

This was the point when I decided to help the AI into compiling its proposal into a full-blown spec.
In that process, I saw my role mainly to be a mentor - like a teacher who provides the student a subject for his thesis and some initial ideas.
We discussed the matter in numerous iterations.
My contribution was to suggest clarifications and arrangement of the final spec.
The solution proposed and the entire wording are an entirely original creation of the AI.
__

Enhance Model Context Protocol with Aspects from Common Object Request Broker Architecture (CORBA)

Abstract

The rise of autonomous AI agents demands an architectural shift away from
the simple, stateless REST paradigm, which was an ergonomic retreat driven by human complexity constraints. This paper argues that the advanced, stateful and strongly-typed Distributed Object Model (DOM) pioneered by CORBA (Common Object Request Broker Architecture) is better suited to the high-assurance, multi-step goals of sophisticated AI planning systems.

We propose CORBA-NG (Agent Object Protocol), an evolution of the Inter-ORB Protocol (IIOP).
CORBA-NG retains the core architectural strengths of native Object Identity, Statefulness, and Bi-Directional Communication while replacing legacy components with modern standards like Protocol Buffers and gRPC.

This refactored protocol integrates new features essential for AI autonomy, including native ACID Transactional Context and mandatory Observability Hooks, creating an infrastructure layer that executes non-functional requirements and frees the LLM to focus purely on semantic planning.
This design represents a necessary step back to architectural depth to enable the future of AI.

1 Introduction

1.1 REST: The Step Back Driven by Human Frailty
1.2 The Post-Developer Era: Why Complexity Doesn't Matter to AI

2 Evolution of CORBA towards An AI-Optimized CORBA-NG Stack

2.1 Quick Background on CORBA
2.2 Basic difference between REST and CORBA for MCP
2.3 Evolution into an **AI-Native
- 2.3.1 What to Keep (The Core Strength)
- 2.3.2 What to Drop (The Human Complexity)
- 2.3.3 What to Add (The Next-Level AI Features)

3. Hypothetic Specification of a Next-Generation CORBA - The Agent Object Protocol

3.1 Overview and Core Philosophy
3.2 Core Technology Choices and Rationale
- 3.2.1 Rationale for Choosing Protocol Buffers (Protobuf)
- 3.2.2 Rationale for Choosing gRPC over HTTP/2
- 3.2.3 The Evolution of IDL in CORBA-NG/Enhanced MCP
3.3 Core CORBA-NG Services and Extensions
- 3.3.1 AgentObjectManager (Object Lifecycle Service)
- 3.3.2 Integrated Transaction Service (ACID)
- 3.3.3 AgentEventListener (Bi-Directional Communication)

4. CORBA-NG Architectural Flow

4.1 Object Lifecycle and IOR-NG Flow
4.2 Transactional Integrity Flow (ACID)
4.3 Asynchronous Event Stream (Callback)

5. Going Beyond: Hypothetical CORBA-NG to Enhanced MCP

5.1 Focus on Transactional Planning
5.2 Native Policy Enforcement
5.3 The ORB as the Reasoning Broker

6 Conclusion: Embracing Complexity for Autonomous Intelligence

References

Appendix: CORBA-NG Protocol Buffer Specification

1 Introduction

In the history of software architecture, the shift from CORBA (Common Object Request Broker Architecture) and IIOP (Internet Inter-ORB Protocol) to REST (Representational State Transfer) and HTTP is
often seen as an evolutionary step. However, this evolution was primarily driven by human simplicity and infrastructure convenience, not ultimate technical power.

The rise of the Model Context Protocol (MCP) and autonomous AI agents presents a fundamental challenge to this paradigm.
For a machine---especially a Large Language Model (LLM) agent that reasons over complex, multi-step goals---the architectural constraints of REST become a bottleneck. It's time to revisit the "too-complex" models ofthe past, as the complexity that crippled human developers is irrelevant to AI. CORBA, the engine of the distributed object model, is precisely the advanced mechanism AI agents need to unlock their full potential.

1.1 REST: The Step Back Driven by Human Frailty

REST and its stateless foundation were a direct reaction to the
complexity of CORBA.

CORBA Failure (for Humans): CORBA aimed to provide Location Transparency and Object Identity, allowing developers to treat remote objects as if they were local. It failed due to:

Overly Complex Specifications: The standards were sprawling and difficult to implement correctly.
Tooling Immaturity: Early ORBs (Object Request Brokers) were buggy and vendor-specific.
The "Distributed Objects Fallacy": Trying to mask the fundamental differences between local and remote calls led to unexpected failures that were hard for humans to debug.

REST's Triumph (for Humans): REST won by embracing simplicity and
statelessness. By using common HTTP verbs and human-readable JSON/XML,
it fit perfectly with web infrastructure and lowered the cognitive
load for human programmers. It reduced distributed computing to a
simple, uniform document-retrieval model.

REST, therefore, was not a technical evolution over CORBA in terms of
expressiveness or architectural depth; it was an ergonomic retreat. We abandoned native state, strong typing, and bi-directional
communication to make the system easier for human beings to manage.

1.2 The Post-Developer Era: Why Complexity Doesn't Matter to AI

The constraints that necessitated the shift to REST vanish when the
developer is the AI agent itself.

Complexity is Context: An LLM agent does not struggle with an extensive Interface Definition Language (IDL) or the nuances of an ORB's policy settings. These complex specifications become part of the agent's context---a high-fidelity, strongly-typed contract it can reason over with precision.
Statefulness is Native: An AI agent performing a complex task like "debug this codebase" or "execute a multi-leg trade" inherently operates in a stateful manner. Wrapping stateless REST calls with custom logic for session tracking (the current MCP reality) introduces boilerplate, risk, and context-window bloat. A native CORBA object model simply is stateful, mirroring the agent's own internal reasoning model.
Reliable Tooling: The ORB's responsibility is to handle object activation, persistence, location, and marshaling---the exact details that humans found painful. For an AI, this becomes a reliable, self-managing infrastructure layer that ensures remote operations behave predictably.

In this paradigm, the AI should choose the most architecturally
expressive protocol, and CORBA's distributed object model offers far
more granularity and control than the simple resource model of REST.

2 Evolution of CORBA towards An AI-Optimized CORBA-NG Stack

2.1 Quick Background on CORBA

The Common Object Request Broker Architecture (CORBA), defined by the Object Management Group (OMG), was the leading distributed object standard of the 1990s. Its primary goal was interoperability: allowing software components written in different languages (e.g., C++, Java, Python) to communicate seamlessly over a network.

Distributed Object Model: CORBA treats remote application components as local objects with unique Identity.
Interface Definition Language (IDL): This is the heart of CORBA. IDL is a language-agnostic contract used to define the methods and data types of an object's interface. It ensures strict, cross-language typing.
Object Request Broker (ORB): The runtime environment required on both the client and server. The ORB handles the complexity of locating the remote object, marshaling (packaging) the data, and dispatching the request.
Interoperable Object Reference (IOR): A globally unique identifier (the handle) used by the client's ORB to locate and communicate with a specific remote object instance.
General Inter-ORB Protocol (GIOP): The abstract specification for message formats and data representation between ORBs.
Internet Inter-ORB Protocol (IIOP): This is GIOP mapped onto TCP/IP. IIOP is the specific binary protocol that carries the CORBA object requests and replies over the Internet, prized for its high performance.

In essence, CORBA provided an architecture where an ORB uses the strongly-typed contract from the IDL to speak the IIOP protocol to send requests to an IOR on a remote server.

2.2 Basic difference between REST and CORBA for MCP

Native Object Identity and Lifetime

Instead of merely calling a POST /execute-tool, the agent instantiates
a remote object and interacts with its unique reference.

The CORBA model introduces the ORB as the central abstraction layer,
allowing the AI's logic to remain high-level, dealing only with object
interfaces (IDL), while the ORB handles the gritty network details.

Bi-Directional Communication (Callbacks)

IIOP allows the tool (Server) to call a method back on the agent
(Client). This is the key to event-driven, non-blocking agent workflows.

Scenario: An AI agent is monitoring a slow, complex database
migration.

Agent Action (Client → Server): migration_tool.start(agent_ref)
(The agent passes its own IOR---agent_ref---to the tool)
Tool Action (Server → Client): agent_ref.notifyStatus('75%
complete')

This is far superior to REST's polling, which wastes resources, or
Webhooks, which are a clumsy, one-way fix for a fundamentally stateless
architecture.

2.3 Evolution into an AI-Native Distributed Object Model

If the CORBA design could be entirely refactored for AI needs, here's how the
CORBA foundation would be evolved to create an optimal, AI-Native
Distributed Object Model.

2.3.1 What to Keep (The Core Strength)

The fundamental architectural components that make CORBA superior to REST
for complex AI agents must be retained and optimized:

Native Object Identity & Statefulness (IOR): Keeps the Interoperable Object Reference (IOR) as a unique, stateful handle for persistent tool instances.
Strong Interface Definition Language (IDL): Retains the formal, language-agnostic contracts for high-fidelity symbolic reasoning about tool capabilities.
Bi-Directional Communication: Preserves native callbacks for event-driven, non-blocking agent workflows.
ORB Abstraction: The Object Request Broker (ORB) remains to manage location, activation, and transport complexity, allowing the AI to focus on high-level goal execution.

2.3.2 What to Drop (The Human Complexity)

The parts that made CORBA cumbersome for human developers and modern
infrastructure must be eliminated:

Bloated Wire Protocol: Drop the legacy encoding in favor of a modern, binary protocol like Protocol Buffers (Protobuf) for efficiency and speed.
Heavy Generated Code: Eliminate complex, error-prone language bindings, moving towards lightweight, automatic serialization/deserialization into native AI data structures.
Opaque CORBA Services: Simplify or replace complex, generalized CORBA services with streamlined AI-Native Discovery and Registry services optimized for LLM tool selection metadata.

2.3.3 What to Add (The Next-Level AI Features)

To provide architectural levers for a sophisticated AI agent, the following features would be added:

Feature to Add	Rationale for AI Agent Sophistication
Integrated Transactional Context	Embed support for ACID transactions directly into the protocol invocation. This allows an AI to guarantee "all or nothing" execution for multi-step, high-value tool calls (e.g., executing a trade), crucial for reliability.
Built-in Tool Versioning & Compatibility	Add mandatory, runtime-queryable versioning metadata to every IOR and IDL interface. The AI can perform more accurate tool selection and graceful degradation based on available features.
Mandatory Observability Hooks	Every call/reply/exception must include structured metadata for tracing and provenance. The ORB generates this trace ID, vital for AI self-debugging, auditing, and post-mortem analysis of failed workflows.
Adaptive Quality-of-Service (QoS) Negotiation	Allow the AI to specify its latency and fault-tolerance requirements in the method invocation header. The ORB dynamically tailors transport, routing, or redundancy to meet the AI's specific, current execution needs.

The Model Context Protocol today focuses on providing a better interface for LLMs (tools/context discovery). Moving it to this evolved CORBA would give it a better engine---a distributed object bus designed for the exact stateful, complex, and high-assurance interactions autonomous AI agents require.
It's time to let AI agents build upon the most sophisticated technical foundations available, regardless of their history with human developers.

3 Hypothetic Specification of a Next-Generation CORBA - The Agent Object Protocol

3.1 Overview and Core Philosophy

CORBA-NG is a proposed evolution of the Internet Inter-ORB Protocol
(CORBA), refactored for the requirements of autonomous AI agents using
the Model Context Protocol (MCP). Its core philosophy is to
integrate native object statefulness, structured events, and
transactional integrity directly into the transport layer, eliminating
the need for AI agents to manually orchestrate these complex concerns
over a stateless protocol like HTTP/REST.

Feature Focus	CORBA-NG Design Goal
Object Model	Maximally Stateful & Transactional
Data Encoding	Maximally Efficient & Strongly Typed
Communication	Naturally Bi-Directional & Event-Driven
Agent Support	Integrated Observability & QoS Negotiation

3.2 Core Technology Choices and Rationale

The CORBA-NG stack eliminates classic CORBA components in favor of modern, efficient alternatives.

Component	Choice	Rationale
Interface Definition	Google Protocol Buffers (Protobuf)	Keep the IDL Strength, Drop the Complexity. Protobuf provides a language-agnostic mechanism for defining strict data structures and service contracts (similar to IDL). It ensures strong typing, which is essential for an AI to reason accurately about inputs/outputs, while generating extremely lightweight code bindings and reducing the boilerplate that plagued legacy CORBA.
Transport Protocol	HTTP/2 or gRPC	Keep the Infrastructure Ubiquity, Drop the Custom Port: Instead of the custom TCP port/stack of classic CORBA, CORBA-NG layers its object model over modern, firewall-friendly infrastructure. gRPC (which uses HTTP/2) natively supports bi-directional streaming and high-speed binary transport, making it an ideal carrier for CORBA-NG's object references and event streams.
Serialization	Protobuf Binary Encoding	Drop the Bloat. This offers superior performance, minimal overhead, and guaranteed cross-language compatibility compared to legacy CORBA encoding or verbose JSON.

3.2.1 Rationale for Choosing Protocol Buffers (Protobuf)

The decision to choose Protocol Buffers (Protobuf) as the Interface Definition Language (IDL) and serialization mechanism for IIOP-NG was based on balancing the strengths of the legacy CORBA IDL (strong typing) with the demands of modern, high-performance, and AI-centric communication.

Below is the rationale and a comparison with key alternatives considered.

a) Strong Typing and Schema Enforcement (The IDL Heritage)

Advantage: Like the classic CORBA IDL, Protobuf requires explicit definition of data types (string, int32, custom messages, etc.) and fields. This strong, explicit typing is non-negotiable for AI agents, as it eliminates ambiguity and allows the LLM to perform high-fidelity symbolic reasoning about the exact structure of inputs and outputs. This prevents runtime errors and hallucinations better than schemaless alternatives.
IIOP-NG Fit: This directly supports the need for the Interface Hash in the IOR-NG to guarantee contract compliance.

b) Binary Efficiency and Speed (The IIOP Heritage)

Advantage: Protobuf serializes data into a compact, binary format. This is significantly smaller and faster to transmit and parse than text-based formats like JSON. This high efficiency is crucial for the high volume, low-latency communication required in large-scale, autonomous agent systems.
IIOP-NG Fit: It honors IIOP's original goal of being a fast, binary wire protocol, shedding the legacy complexity of GIOP encoding while retaining speed.

c) Tooling and Modern Ecosystem (The gRPC Advantage)

Advantage: Protobuf is the native and canonical IDL for gRPC, which we chose as the underlying transport for IIOP-NG (layering over HTTP/2). By using Protobuf, we gain automatic, robust, and mature code generation in dozens of languages. This tooling automatically creates the necessary client stubs and server skeletons, reducing boilerplate and implementation complexity that plagued classic CORBA.
IIOP-NG Fit: This ensures rapid adoption and integration by the tools and ORBs built by different agent frameworks.

Alternatives Considered

We considered two primary modern alternatives that are widely used for inter-service communication: JSON Schema/REST and Apache Avro.

Alternative	Role/Mechanism	Why It Was NOT Chosen for IIOP-NG
JSON Schema (for REST/OpenAPI)	Text-based, human-readable data format with an external schema definition.	Stateless Constraint: JSON/REST is inherently stateless, which directly contradicts the core IIOP-NG goal of native object statefulness. Efficiency: Text-based encoding is larger and slower to parse than binary formats, increasing latency.
Apache Avro	Binary data serialization system with a schema stored alongside the data or managed centrally.	Tooling Complexity: While offering excellent schema evolution features (which Protobuf handles via field numbering), Avro's tooling and language support are generally less mature and widely adopted than Protobuf/gRPC, particularly in the core AI/ML ecosystem. Focus: Avro is often optimized for batch processing and data storage rather than low-latency RPC.

Conclusion on Alternatives:

JSON Schema/REST was rejected because its stateless, resource-centric nature and poor efficiency directly undermined the need for a stateful, high-performance, distributed object model.
Apache Avro was considered a strong contender but was ultimately rejected in favor of Protobuf due to superior ecosystem maturity, cleaner gRPC integration, and broader adoption in the modern microservices and AI environment, minimizing friction for implementers.

3.2.2 Rationale for Choosing gRPC over HTTP/2

gRPC was selected as the foundational transport for IIOP-NG because it directly provides the native features required by a distributed object model that legacy protocols (like HTTP/1.1) lack.

a) Native Bi-Directional Streaming (Honoring Callbacks)

IIOP Requirement: Classic IIOP's strength was supporting bi-directional calls (callbacks), allowing the server to asynchronously contact the client.
gRPC Advantage: gRPC, built on HTTP/2, natively supports four types of RPC, including Server Streaming and Bi-directional Streaming. This directly enables the crucial AgentEventListener service in IIOP-NG, allowing tool objects to send real-time, structured events back to the AI agent without requiring inefficient polling or complex WebSocket tunneling. This functionality is essential for non-blocking, autonomous agent workflows.

b) Performance and Efficiency (Honoring Binary Transport)

IIOP Requirement: IIOP was designed for high-speed, binary exchange via GIOP over TCP/IP.
gRPC Advantage: gRPC uses Protobuf's compact binary serialization (as chosen previously) and leverages HTTP/2's features (header compression, multiplexing a single TCP connection). This combination drastically reduces latency and resource consumption compared to traditional HTTP/1.1 or JSON, aligning perfectly with IIOP's original performance mandate.

c) Strong Contract Enforcement (The ORB's Role)

IIOP Requirement: The ORB enforces the contract defined by the IDL.
gRPC Advantage: gRPC is contract-driven; the client and server stubs are generated directly from the Protobuf definition. This ensures that the messages sent over the wire strictly adhere to the defined schema, making the ORB-NG's job of marshaling/unmarshaling predictable and reliable—a necessary condition for high-assurance AI transactions.

Alternatives Considered

The primary alternatives considered were REST over HTTP/1.1 (the status quo for MCP) and WebSockets.

Alternative	Mechanism	Why It Was NOT Chosen for IIOP-NG
REST over HTTP/1.1	Stateless request/response over text-based JSON.	Stateless Impediment: Directly contradicts the core IIOP-NG requirement for native statefulness. It forces the AI to manually manage sessions and context. Inefficiency: Lacks multiplexing (requires multiple TCP connections) and relies on slow text-based JSON, degrading performance for high-throughput AI systems.
WebSockets	Bi-directional, full-duplex persistent connection, often carrying raw JSON or XML.	Lack of Structure: While providing bi-directional communication, WebSockets are a raw, message-passing channel. They lack built-in RPC semantics and require a custom layer (like WAMP or a custom JSON-RPC implementation) to define the structure and method invocation, leading to implementation variability and weaker type enforcement.
Vanilla TCP Sockets	Raw, low-level connection over TCP.	Overly Complex: While mirroring classic IIOP's foundation, using raw TCP requires the ORB-NG to manually implement HTTP/2's features (like security, routing, load balancing, and multiplexing). This reintroduces the heavy, proprietary ORB complexity that made classic CORBA difficult for humans (and unnecessarily complex for general AI tool providers).

Conclusion on Alternatives:

gRPC provides the optimal balance by retaining the binary efficiency and native bi-directionality of IIOP (which WebSockets and REST lack), while integrating cleanly with Protobuf's strong contract enforcement and leveraging the widespread infrastructure benefits of HTTP/2 (which raw TCP lacks). It is the most robust foundation for building the transactional, stateful IIOP-NG protocol for an Enhanced MCP.

3.2.3 The Evolution of IDL in CORBA-NG/ Enhanced MCP

The classic Interface Definition Language (IDL) doesn't disappear; it evolves and merges with Protocol Buffers (Protobuf) and the Model Context Protocol (MCP) metadata.

The purpose of IDL—defining a language-agnostic contract—is preserved, but its format and output are modernized for machine reasoning and efficient transport.

a) The Technical Contract: Protobuf is the New IDL

The primary function of IDL—defining the exact structure of data types, method signatures, and exceptions—is fulfilled by the Protocol Buffer (.proto) file.

Language Agnosticism: Just like IDL, Protobuf is language-neutral, allowing ORBs and agents written in Python, Java, Go, etc., to interoperate perfectly.
Strong Typing: Protobuf mandates explicit data types (e.g., int32, string, bool), which is essential for the AI's symbolic reasoning phase. An agent can reliably determine the exact format of input and output without ambiguity.
Method Definition: Protobuf's service definition directly maps to CORBA's interface definition, providing the formal Remote Procedure Call (RPC) contract.
Efficiency: Protobuf compiles into binary serialization, eliminating the runtime overhead and size bloat associated with legacy IDL compilation and encoding rules.

b) The Agent Reasoning Contract: MCP Metadata

While Protobuf defines what data looks like, it doesn't define why the method is useful for an LLM. This is where the MCP specification augments the Protobuf contract.

The IDL's descriptive elements are moved into structured annotations within the .proto file or a linked MCP definition file.

IDL Aspect	MCP Representation	Purpose for AI Agent
Method Description	// @mcp.description Protobuf Annotation	Provides the LLM with the necessary natural language prompt (the "why") to decide if the tool is relevant.
Interface Version	tool_version in the IOR-NG	Allows the agent to use versioning logic (e.g., "I need feature X, only available in version 2.0 and up").
Exceptions/Errors	Status Message (Standard gRPC/Protobuf)	Provides a machine-readable error code and message, enabling the agent to plan for reliable error recovery (e.g., if code 403, try refreshing the security token).

c) IOR-NG: The Runtime IDL Guarantee

The Interface Hash field within the IOR-NG (Interoperable Object Reference - Next Generation) serves as a runtime guarantee of the IDL.

When an AI agent receives an IOR-NG, it checks the interface_hash against its local copy of the expected Protobuf schema.
If the hashes match, the agent knows with certainty that the remote object adheres to the exact contract it expects, ensuring protocol safety and preventing runtime type errors that would confuse the planning LLM.

In summary, Protobuf is the new IDL's syntax and serialization engine, while MCP Metadata is the new IDL's semantic layer for AI consumption. This merger provides both the rigorous technical contract required for IIOP-NG's high-assurance transport and the high-fidelity semantic context required for LLM planning.

3.3 Core CORBA-NG Services and Extensions

3.3.1 AgentObjectManager (Object Lifecycle Service)

The AgentObjectManager is the base service that every CORBA-NG server MUST implement to manage stateful object activation, lease, and cleanup.

RPC Method	Purpose	Key Feature
`DiscoverTools`	Tool discovery (Replaces CORBA Naming Service)	Supplies metadata for LLM tool selection
`CreateObject`	Instantiation of a new, stateful IOR-NG instance	Native Object Identity and Lifetime
`DestroyObject`	Explicitly terminates the remote object and frees resources	Clean Resource Management
`RenewLease`	Used by the Client ORB-NG for periodic lease renewal	Prevents Resource Leaks from non-responsive agents

3.3.2 Integrated Transaction Service (ACID)

The optional but recommended TransactionManager service enables the AI to reason over and execute ACID-compliant workflows for complex, multi-tool actions.

RPC Method	Purpose
`StartTransaction`	Starts a new distributed transaction.
`Prepare`	Phase 1: Prepares all involved IOR-NGs for commit.
`Commit`	Phase 2: Commits the transaction. MUST roll back if any object fails.
`Rollback`	Forces an immediate rollback.

3.3.3 AgentEventListener (Bi-Directional Communication)

The AgentEventListener service is implemented by the AI Agent Host (Client), allowing the Tool Service to proactively stream structured events back to the agent.

The agent's IOR-NG is passed via the CallContext's callback_ref.
The server uses a gRPC streaming RPC to send events (e.g., STATUS_UPDATE, CRITICAL_ERROR) back to the client.

* This mechanism is key to event-driven, non-blocking agent workflows.

4 CORBA-NG Architectural Flow

The CORBA-NG design guarantees explicit lifecycle management and complex transactional integrity, which are abstracted from the AI agent's planning logic.

4.1 Object Lifecycle and IOR-NG Flow

The most critical difference from REST is the stateful object
lifecycle. This sequence diagram details the process an AI Agent takes
to acquire and destroy a stateful tool object.
It formally specifies the required
handshake (steps 2-5) for object activation and the explicit
termination (steps 10-12), ensuring the AI correctly plans for
resource allocation and cleanup.

4.2 Transactional Integrity Flow (ACID)

This graph specifies the non-blocking, two-phase commit protocol for
ensuring complex, multi-tool actions are Atomic.
It provides the logic gates and required
state transitions for the ORB and tools, explicitly detailing the
two-phase commit contract that the AI must rely on for reliable,
high-value operations.

4.3 Asynchronous Event Stream (Callback)

This diagram details the crucial bi-directional communication
mechanism, allowing the server to asynchronously update the agent,
removing the need for wasteful polling.
It codifies the non-blocking nature of the
protocol and the inversion of control enabled by the CallbackRef,
demonstrating to the AI that it can immediately move on to the next step
while relying on asynchronous updates for completion.

5. Going Beyond: HCORBA-NG to Enhanced MCP

Moving from the current MCP paradigm to a fully realized Enhanced MCP using CORBA-NG means shifting from simply describing tool-use to
guaranteeing complex agent behavior.

5.1 Focus on Transactional Planning

MCP: Agents deal with individual, "best-effort" function calls. Failure is a complex recovery problem.
Enhanced MCP (via CORBA-NG): Agents can now treat multi-step tasks as single, guaranteed operations. The Transaction Manager enables the agent's planner to shift from fragile, step-by-step logic to reliable, high-assurance ACID-based workflows (e.g., "Either the deploy succeeds, or the system reverts cleanly"). This drastically increases agent trustworthiness for critical tasks.

5.2 Native Policy Enforcement

MCP: Security and authorization are typically handled outside the protocol (e.g., API Gateway).
Enhanced MCP (via CORBA-NG): The ORB-NG becomes a policyenforcement point. The CallContext can carry required security tokens and QoS preferences. The ORB, as the central broker, is ideally positioned to enforce access control, rate limiting, and resource allocation before the call hits the tool object. This is essential for robust, multi-agent systems where trust boundaries are dynamic.

5.3 The ORB as the Reasoning Broker

MCP: The agent's LLM must do all the heavy lifting of figuring out how to use the tool based on technical docs.
Enhanced MCP (via CORBA-NG): The ORB-NG can become a reasoning layer. By inspecting the required Tool Version and the requested QoS Preference, the ORB itself could dynamically route the request:
- To a faster, specialized implementation if LOW_LATENCY is requested.
- To a highly redundant service mesh if HIGH_RELIABILITY is requested.

This means the agent's planner can specify a high-level goal, and the
Enhanced MCP infrastructure executes the non-functional requirements,
freeing the LLM to focus purely on semantic planning.

6 Conclusion: Embracing Complexity for Autonomous Intelligence

The design of the Agent Object Protocol (CORBA-NG) is an explicit rejection of architectural simplicity for the sake of human convenience. The Distributed Object Model, with its native statefulness, bi-directional communication (callbacks), and integrated transactional integrity, introduces a level of complexity that proved unsustainable for human-driven development two decades ago.

However, this complexity is not a defect; it is a feature necessary for true autonomous intelligence. In the Post-Developer Era, the implementation burden is effectively shifted to the AI agent itself. The Large Language Model (LLM) agent, operating as both the architect and the end-user, can consume the highly structured Protocol Buffer IDL and ORB policy settings not as an implementation nightmare, but as high-fidelity context for symbolic reasoning. The AI is designed to thrive on the structured depth that human developers sought to avoid.

The ORB-NG evolves into a self-managing infrastructure layer, responsible for executing complex non-functional requirements like QoS negotiation, observability tracing, and ACID two-phase commits. By handling these intricate, non-semantic concerns at the protocol level, the CORBA-NG architecture executes the non-functional requirements, freeing the agent's LLM planner from low-level infrastructure management to focus purely on achieving high-level goals with guaranteed reliability and assurance. This return to a more expressive, stateful architecture is not a regression, but an essential step forward in unlocking the full potential of high-assurance, autonomous AI systems within the Model Context Protocol.

References

This document presents a conceptual evolution of the Internet Inter-ORB Protocol (IIOP) for autonomous AI agents, drawing heavily upon foundational distributed computing standards, modern internet protocols, and software architecture principles.

I. Foundational Distributed Object Computing (CORBA & IIOP)

These references define the legacy architecture and protocols that serve as the conceptual basis for the CORBA-NG proposal.

Object Management Group (OMG). The Common Object Request Broker Architecture and Specification (CORBA), Version 2.0. (1995 and later revisions).
- Cited for: Defining the foundational concepts of the Object Request Broker (ORB), Object Identity, and the original Interface Definition Language (IDL).
- Link to Resource
Object Management Group (OMG). Interoperable Object Reference (IOR) Specification.
- Cited for: The stateful mechanism for identifying and locating remote objects, which is the progenitor of the proposed IOR-NG.
- Link to Resource
Object Management Group (OMG). CORBAservices: Common Object Services Specification. (Specifically for the Transaction Service).
- Cited for: The original concept of integrated, distributed services for managing tasks like Transactions and Events.
- Link to Resource

II. Modern Architectural Paradigms (REST and gRPC)

These references relate to the architectures discussed as alternatives and the modern foundation chosen for the CORBA-NG stack.

Fielding, R. T. Architectural Styles and the Design of Network-based Software Architectures. Doctoral Dissertation, University of California, Irvine. (2000).
- Cited for: Defining Representational State Transfer (REST) and the principles of Statelessness used in modern web APIs (MCP architecture).
- Link to Resource
Google. gRPC Documentation.
- Cited for: The choice of gRPC (layered over HTTP/2) as the high-performance transport, providing native Bi-Directional Streaming and efficient Remote Procedure Call (RPC) semantics for the ORB-NG.
- Link to Resource

III. Data Serialization and Contract Enforcement

This section addresses the move from CORBA IDL to the modern, efficient Protobuf stack.

Google. Protocol Buffers (Protobuf) Documentation.
- Cited for: The adoption of Protobuf as the new, efficient binary wire format and the Interface Definition Language (replacing CORBA IDL) for strong, machine-readable contracts.
- Link to Resource

IV. Transactional Integrity and Reliability

This addresses the ACID principles integrated into the proposed CORBA-NG transactional flow.

Haerder, T. and Reuter, A. Principles of Transaction-Oriented Database Recovery. ACM Computing Surveys (CSUR), Vol. 15, No. 4. (1983).
- Cited for: Defining the ACID (Atomicity, Consistency, Isolation, Durability) properties, which underpin the Transactional Integrity Flow proposed for high-assurance AI operations.
- Link to Resource

V. Agent-Protocol Interaction (Model Context Protocol)

This reference defines the current state-of-the-art interaction paradigm that the CORBA-NG proposal seeks to evolve into Enhanced MCP.

Industry Standard. The Model Context Protocol Framework (MCP).
- Cited for: Defining the conceptual standard for LLM Tool Discovery and Context Management, which serves as the current paradigm the proposed CORBA-NG architecture (Enhanced MCP) aims to replace and enhance with native statefulness and transactional integrity.
- Link to Resource ___ # Appendix: CORBA-NG Protocol Buffer Specification

The Agent Object Protocol (CORBA-NG) uses Protocol Buffers (Protobuf) as its Interface Definition Language (IDL) and wire format, replacing the legacy CORBA IDL and GIOP encoding. Protobuf provides strict, language-agnostic contracts that the AI agent can reason over, while offering performance and efficiency superior to text-based formats like JSON.

Protobuf Rationale and Implementation

When a .proto file (like the one below) is compiled, it automatically generates native code classes in various languages (Java, Python, C++, Go, etc.).

In Java: The compiler generates a concrete Java class for each message (e.g., IorNg becomes com.corbang.IorNg.java). These classes provide strong type safety, efficient builders for creating objects, and methods for serializing/deserializing the objects to and from binary streams. The ORB-NG client stub would use these generated classes to easily build and send the InvocationHeader.
In Python: The compiler generates Python modules containing classes that allow creation, manipulation, and serialization of the messages. This means AI tooling can interact with objects like CallContext using native Python classes, abstracting away the binary serialization complexity for the LLM's planning logic.

This automatic code generation offloads the complexity of marshaling and unmarshaling from both the human developer and the AI's internal logic, embedding the required complexity directly into the efficient ORB-NG runtime.

Protobuf Definition (`iiop_ng.proto`)

protobuf:CORBA-NG Message Specification:iiop_ng.proto
syntax = "proto3";

package iiop_ng;

// --- Enums and Base Types ---

// Defines the desired Quality of Service for an invocation.
enum QosPreference {
QOS_BEST_EFFORT = 0; // Default: Fast, standard reliability.
QOS_LOW_LATENCY = 1; // Prioritize speed, potentially lower reliability guarantees.
QOS_HIGH_RELIABILITY = 2; // Prioritize guaranteed delivery/retry logic.
QOS_HIGH_ASSURANCE_ACID = 3; // Transactional guarantee required.
}

// Interoperable Object Reference - Next Generation
// The stateful, globally unique handle for a remote object instance.
message IorNg {
string host = 1;
int32 port = 2;
string object_id = 3;         // Unique ID for the specific instance (e.g., F_23X)
bytes interface_hash = 4;     // MD5/SHA hash of the Protobuf service definition (IDL contract)
string tool_version = 5;      // Version of the underlying tool implementation (e.g., "2.0")
optional string agent_callback_host = 6; // Host/Port for bi-directional communication (used for callbacks)
}

// --- Request and Context Messages ---

// Standard context attached to every remote invocation by the ORB-NG.
message CallContext {
string observability_trace_id = 1; // Unique ID for tracing across services.
QosPreference qos_preference = 2; // Agent's requested QoS for this call.
optional string transaction_id = 3; // Mandatory for ACID operations.
optional IorNg agent_callback_ref = 4; // The agent's own IOR for receiving events.
}

// The header for a remote method invocation (e.g., ReadLine() or UpdateData()).
message InvocationHeader {
IorNg target = 1;         // The IOR of the object being called.
string method_name = 2;   // The string name of the method to invoke.
CallContext context = 3;  // The standard context data.
bytes parameters = 4;     // Serialized message containing the method arguments.
}

// --- Transaction Manager Messages (ACID Flow) ---

// Sent by the Transaction Manager to tools to prepare for commit.
message PrepareRequest {
string transaction_id = 1;
}

// Response from the tool object indicating readiness to commit.
message PrepareResponse {
string transaction_id = 1;
bool vote_ok = 2; // True if the tool can guarantee commit (Vote Yes).
optional string error_message = 3;
}

// Final command to make the changes permanent.
message CommitRequest {
string transaction_id = 1;
}

// Final command to discard all changes made during the transaction.
message RollbackRequest {
string transaction_id = 1;
}

// --- Event Stream Messages (Callbacks) ---

// Message used for bi-directional streaming from the tool back to the agent.
message ToolEvent {
string source_object_id = 1;
string event_type = 2; // e.g., "STATUS_UPDATE", "OPERATION_COMPLETE", "ERROR"
int64 timestamp_ms = 3;
bytes payload = 4; // Serialized message specific to the event type (e.g., JobStatus)
}

DEV Community: Christoph Grimm

Improve your Prompt Engineering using Mermaid Diagrams

Summary

Why Mermaid Syntax Helps an LLM

Example: Describing a Purchase Order Process

Textual Representation

Complementary Mermaid Sequence Diagram (DSL)

The combined input compensates for the core weaknesses of the LLM:

Place the diagram first and the textual description afterwards!

Appendix: Mermaid Diagram Types you should consider for your Prompts

Enhance Model Context Protocol with Aspects from Common Object Request Broker Architecture (CORBA)

Co-Author's Note

Enhance Model Context Protocol with Aspects from Common Object Request Broker Architecture (CORBA)

Abstract

Table of Contents

1 Introduction

1.1 REST: The Step Back Driven by Human Frailty

1.2 The Post-Developer Era: Why Complexity Doesn't Matter to AI

2 Evolution of CORBA towards An AI-Optimized CORBA-NG Stack

2.1 Quick Background on CORBA

2.2 Basic difference between REST and CORBA for MCP

2.3 Evolution into an AI-Native Distributed Object Model

2.3.1 What to Keep (The Core Strength)

2.3.2 What to Drop (The Human Complexity)

2.3.3 What to Add (The Next-Level AI Features)

3 Hypothetic Specification of a Next-Generation CORBA - The Agent Object Protocol

3.1 Overview and Core Philosophy

3.2 Core Technology Choices and Rationale

3.2.1 Rationale for Choosing Protocol Buffers (Protobuf)

3.2.2 Rationale for Choosing gRPC over HTTP/2

3.2.3 The Evolution of IDL in CORBA-NG/ Enhanced MCP

3.3 Core CORBA-NG Services and Extensions

3.3.1 AgentObjectManager (Object Lifecycle Service)

3.3.2 Integrated Transaction Service (ACID)

3.3.3 AgentEventListener (Bi-Directional Communication)

* This mechanism is key to event-driven, non-blocking agent workflows.

4 CORBA-NG Architectural Flow

4.1 Object Lifecycle and IOR-NG Flow

4.2 Transactional Integrity Flow (ACID)

4.3 Asynchronous Event Stream (Callback)

5. Going Beyond: HCORBA-NG to Enhanced MCP

5.1 Focus on Transactional Planning

5.2 Native Policy Enforcement

5.3 The ORB as the Reasoning Broker

6 Conclusion: Embracing Complexity for Autonomous Intelligence

References

I. Foundational Distributed Object Computing (CORBA & IIOP)

II. Modern Architectural Paradigms (REST and gRPC)

III. Data Serialization and Contract Enforcement

IV. Transactional Integrity and Reliability

V. Agent-Protocol Interaction (Model Context Protocol)

Protobuf Rationale and Implementation

Protobuf Definition (iiop_ng.proto)

Protobuf Definition (`iiop_ng.proto`)