DEV Community: Kwstas gal08

Building a Protocol Translation Layer for Multi-Agent Systems

Kwstas gal08 — Mon, 23 Mar 2026 14:26:07 +0000

Modern software systems don’t struggle because they lack APIs.

They struggle because APIs don’t understand each other.

Different platforms use different protocols, different schemas, different message formats, and different semantic meanings. Even when two systems expose clean APIs, integrating them often requires writing layers of glue code, adapters, transformers, and custom logic.

This becomes even more complicated in multi-agent and distributed environments, where autonomous systems need to collaborate, exchange data, and execute tasks across platforms in real time.

So the real problem isn’t communication.

It’s understanding.

This article explores how to build a protocol translation layer for multi-agent systems, the architectural decisions behind it, and a reference implementation that demonstrates the approach.

The Problem: Systems Speak Different Languages

Imagine multiple agents interacting across platforms:

a trading system
a market data provider
a payment processor
a prediction engine
a task automation service

Each one exposes:

different protocols
different schemas
different message formats
different meanings for similar fields

For example:

Platform A:
price: 100
symbol: BTC

Platform B:
value: 100
asset: BTC

Platform C:
amount: 100
currency: BTC

Structurally similar.

Semantically different.

This creates three major problems:

1. Protocol mismatch

Systems use REST, WebSockets, message queues, or custom protocols.

2. Schema mismatch

Field names and structures vary.

3. Semantic mismatch

Even identical fields may mean different things.

Traditional integration approaches don’t scale well.

You end up writing:

adapters
translators
middleware
custom handlers

for every new system.

That leads to fragile and complex infrastructure.

The Idea: Protocol Translation Layer

Instead of writing integrations between every system, we introduce a protocol translation layer.

This layer acts as an intermediary that:

translates protocols
maps semantic fields
routes messages
coordinates agents
normalizes data
orchestrates tasks

Architecture concept:

Agent A
   |
Protocol Adapter
   |
Translation Layer
   |
Semantic Mapping
   |
Orchestrator
   |
Protocol Adapter
   |
Agent B

This allows agents to interact without knowing each other's internal structure.

The translation layer becomes the universal communication bridge.

Core Components of a Protocol Translation System

To make this work, we need several architectural components.

1. Protocol Adapters

Protocol adapters convert external communication formats into a unified internal representation.

Examples:

REST → internal message
WebSocket → internal event
Queue → internal task
HTTP → structured message

This ensures all incoming data follows a consistent format.

Incoming Request
      ↓
Protocol Adapter
      ↓
Unified Message

Now the system can process messages consistently.

2. Semantic Field Mapping

This is the most important part.

Protocol translation is not just format conversion.

It requires understanding meaning.

Example:

price → value
symbol → asset
amount → quantity

The system must know that these represent the same concept.

This is achieved through:

JSON schema validation
ontologies
rule engines
mapping templates
ML fallback mapping

The translation layer becomes semantically aware.

3. Agent Registry and Discovery

In a distributed system, agents need to find each other.

An agent registry stores:

agent capabilities
supported protocols
semantic fields
reliability scores
endpoints

Discovery scoring helps determine:

which agent should handle a task
which system is most reliable
which route is optimal

This allows dynamic collaboration.

4. Async Orchestration

Synchronous systems fail quickly in distributed environments.

Instead, we use:

task queues
async execution
retry logic
message leasing
multi-hop routing

Flow example:

Request → Queue → Agent → Processing → Response

Benefits:

resilience
scalability
fault tolerance
better performance

Async orchestration becomes the backbone of the system.

5. Multi-Hop Routing

Sometimes one agent cannot complete a task.

The system routes it to another agent.

Example:

Agent A → Agent B → Agent C

Each step transforms or enriches the data.

This creates a collaborative execution chain.

The translation layer manages this routing automatically.

6. Observability and Monitoring

Distributed systems need visibility.

Key components:

structured logging
metrics
dashboards
error tracking

This enables:

performance monitoring
debugging
reliability tracking
system health analysis

Without observability, protocol translation systems become impossible to maintain.

Real-World Use Cases

This architecture supports several practical scenarios.

Cross-protocol collaboration

Different platforms communicate seamlessly.

Multi-platform trading

Trading agents interact across exchanges.

Data normalization

Market data is unified into a common structure.

Automated task handoffs

Agents pass tasks between each other.

Market forecasting

Prediction engines feed execution systems.

Financial orchestration

Multiple services coordinate operations.

This turns distributed systems into cooperative ecosystems.

Reference Implementation

To explore this architecture in practice, I built an open-source reference implementation called Engram.

It demonstrates:

protocol translation
semantic field mapping
agent registry
async orchestration
multi-hop routing
live data feeds
monitoring and observability
structured logging
Swagger API interface
real-time translation playground

The project is written primarily in JavaScript, with supporting components in Python, and is released under the MIT license.

Repository

You can explore the full implementation here:

GitHub Repository: Engram

The goal of the project is not to be a finished product, but a reference architecture for building protocol translation and multi-agent coordination systems.

Key Design Lessons

Building a protocol translation layer revealed several important lessons.

1. Semantics matter more than structure

Schema translation alone is not enough.

Meaning must be preserved.

2. Async systems scale better

Queues and orchestration make systems resilient.

3. Discovery is critical

Agents must find each other dynamically.

Static integrations do not scale.

4. Observability is essential

Without monitoring, distributed systems fail silently.

5. Protocol translation enables autonomy

Agents can operate independently while still collaborating.

This is the foundation of intelligent distributed systems.

Future Directions

Protocol translation layers can evolve into:

autonomous agent networks
self-organizing systems
predictive execution engines
decentralized service ecosystems
real-time financial orchestration platforms

As systems grow more complex, interoperability will become the most important infrastructure layer.

Protocol translation is one step toward that future.

Conclusion

Modern distributed systems need more than APIs.

They need:

semantic understanding
protocol translation
agent coordination
async orchestration
intelligent routing

A protocol translation layer provides this foundation.

Instead of building fragile integrations, we can build cooperative systems that understand each other.

The Engram project is a small step in that direction and serves as a reference for developers exploring multi-agent and protocol translation architectures.

Discussion

How would you design a protocol translation system for distributed agents?

Would you rely more on ontologies, ML-based mapping, or rule engines?

I’d love to hear your thoughts and approaches.

I Built the Universal Translator for MCP, A2A, and ACP Agents — So They Can Finally Collaborate (Docker + Live Swagger Demo)

Kwstas gal08 — Tue, 17 Mar 2026 16:44:05 +0000

I Built the Universal Translator for MCP, A2A, and ACP Agents — So They Can Finally Collaborate (Docker + Live Swagger Demo)

If you’ve tried building a real multi-agent system in 2026, you already know the pain.

Anthropic’s MCP agent wants to hand off a task.

Google’s A2A agent expects a completely different envelope.

IBM’s ACP agent uses yet another schema.

Result? You spend weeks writing custom adapters instead of actually building agents.

I got tired of it. So in a few weeks of vibe-coding I shipped a middleware bridge that translates protocols and resolves semantic differences automatically.

It’s open source, one-command to run, and already has a live Swagger UI. Here’s how it works and why it might save you months of pain.

The 2026 Agent Protocol Wars (yes, it’s that bad)

Right now the ecosystem is exploding:

MCP (Anthropic) — great for model context and tools
A2A (Google) — focused on direct agent-to-agent handoffs with Agent Cards
ACP (IBM) — structured coordination in shared environments

They’re all excellent… in isolation.

Cross-protocol? Manual JSON mapping hell.

This is the exact fragmentation everyone is complaining about on Reddit, HN, and Medium right now.

The Solution: Engram Translator Middleware

One lightweight FastAPI service that sits in the middle and does two things:

Protocol Translation — converts envelopes between MCP ↔ A2A ↔ ACP
Semantic Mapping — uses OWL ontologies + PyDatalog + JSON Schema + ML fallback to fix field mismatches (e.g. user_info.name → profile.fullname)

Plus built-in:

Agent Registry & Discovery (with compatibility scores)
Async orchestration + retries
JWT auth layer

Full architecture diagram:

``mermaid
flowchart LR
    A[Source Agent\nProtocol: MCP] -->|Task Request| B(Translator\nMiddleware)
    B -->|Protocol & Semantic Translation| C[Target Agent\nProtocol: A2A]`

Try It in 60 Seconds

`bash docker compose up --build`

That’s it.
Swagger UI lives at http://localhost:8000/docs

You can immediately:

Register agents
Discover compatible collaborators
Translate messages across protocols

Real example (from the README):

`bash

# Register an A2A agent
curl -X POST http://localhost:8000/api/v1/register \
  -H "Content-Type: application/json" \
  -d '{"agent_id":"agent-a","endpoint_url":"http://agent-a:8080","supported_protocols":["a2a"],"semantic_tags":["scheduling"]}'

# Send a meeting request from an MCP agent to that A2A agent
curl -X POST http://localhost:8000/api/v1/translate \
  -H "Authorization: Bearer <your-jwt>" \
  -d '{"source_agent":"agent-b","target_agent":"agent-a","payload":{"intent":"schedule_meeting"}}'`

The middleware handles the translation and returns the properly mapped payload.

Why This Actually Works

in ProductionSemantic engine falls back to ML when rules aren’t enough
Registry gives you real-time discovery (compatibility score 0.95 = green light)
Redis + Neon backend for scale
Full tests + GitHub Actions already running

It’s the missing “TCP/IP layer” for agents that everyone keeps asking for.The Bigger VisionThis is the core piece of the open-source Engram agent-economy stack I’m building.

The goal: any agent, any protocol, seamless collaboration.

Try It & Star the Project
Full landing page (more details + vision):
https://www.useengram.com
GitHub repo(star it ):
https://github.com/kwstx/engram_translator

Try the Docker demo and let me know:Does the semantic mapping catch your use-case?
What protocols are you fighting with right now?

I read every comment and I’m actively shipping fixes.
If this saves you even one day of integration hell, smash that — it helps the repo reach more builders who are stuck in the same spot.Happy to accept PRs on custom ontology rules or new protocol support too.Let’s finally make agents talk to each other.#ai-agents

multi-agent #mcp #a2a #acp #interoperability

I built a VS Code extension to give AI long-term memory

Kwstas gal08 — Thu, 15 Jan 2026 15:25:39 +0000

The Problem: The "Regression Loop"
I use Cursor and Copilot daily. They are incredible, but they suffer from Goldfish Memory. If I tell Cursor: "Don't use fs.readFileSync in the frontend," it listens... for that chat session. Three days later, in a new file, it suggests fs.readFileSync again.

I found myself reviewing code for the same bugs repeatedly:

Hallucinated imports
Leaked API keys (regex patterns)
Inefficient React useEffect loops
Deprecated internal functions
The Solution: A "Shadow Guard"
I didn't want another AI wrapper. I wanted a Guardrail System. I built Engram to act as a "Hippocampus" (Memory) for the AI's "Cortex" (Reasoning).

How it works (Technical)
Observation: Engram listens to vscode.workspace.onDidChangeTextDocument.
Fingerprinting: When I reject code (or manually add a rule), Engram stores a "Mistake Fingerprint" (Regex or Vector).
Interception: As the AI streams text into the buffer, Engram scans the incoming characters against its local database.
Latency: <10ms (Local Regex).
Enforcement: If a match is found, it triggers a vscode.Diagnostic (Red Squiggle) immediately.

// Simplified Logic
export class ShadowScanner {
    public scan(document: TextDocument) {
         // Runs in <2ms
        const mistakes = this.detector.findMatches(document.getText());
        const diagnostics = mistakes.map(m => new Diagnostic(
            m.range,
            `Engram Guard: ${m.message}`,
            DiagnosticSeverity.Error
        ));
        this.collection.set(document.uri, diagnostics);
    }
}

The "AI Whisperer" (Context Injection)
Blocking code is good, but teaching the AI is better. Engram hooks into the configuration files of your AI tools to prevent mistakes before they are generated.

Cursor: It automatically appends rules to
.cursorrules.
Copilot: It updates .github/copilot-instructions.md.
When you mark a pattern as "Forbidden", Engram whispers to Cursor:

[System Context] User strictly forbids 'console.log' in production files.

Open Source & Community Rules
I realized I couldn't find every bad pattern myself. So I open-sourced the rule definitions. You can now pull "Community Packs" for various stacks:

React Pack: Blocks common hook infinite loops.
Security Pack: Blocks AWS/Stripe key patterns.
Python Pack: Blocks reckless pandas iterators.

Engram: [https://github.com/kwstx/engram-rules]

Try it out
It's open source, local-first, and designed to keep you in the flow state. If you are tired of fixing the same AI bugs twice, give it a shot.

[https://marketplace.visualstudio.com/items?itemName=use-engram.engram] [https://github.com/kwstx/Engram]