DEV Community: Dee.Bee

⭐ From AI Chaos to AI Governance-The AWS Agent Registry Story: Why discovery, governance, and reuse will define the next generation of AI platforms

Dee.Bee — Mon, 05 Oct 2026 20:10:36 +0000

The Agent Explosion Has Begun
Every enterprise is building AI agents.

A customer support team creates a ticket-resolution agent.

The finance department develops an invoice-processing agent.

Developers build coding assistants.

Operations teams automate workflows using specialized tools and MCP servers.

At first, everything looks exciting.

Then reality hits.

Nobody knows:

Which agents already exist
Who owns them
Whether they passed security review
Which version is running
Whether another team already solved the same problem

What starts as innovation quickly becomes agent sprawl.

This is exactly the challenge AWS Agent Registry was designed to solve: creating a single governed catalog for agents, tools, skills, MCP servers, and custom resources across an enterprise.

From App Stores to Agent Stores
Think about how smartphones evolved.

Before app stores, software was scattered across websites. Finding trusted applications was difficult.

The App Store changed everything.

It introduced:

Discovery
Governance
Ownership
Versioning
Trust

AWS Agent Registry aims to do the same for AI agents.

Instead of asking:

Can we build another agent?

Organizations begin asking:

Does an agent already exist that we can reuse?

That subtle shift can save millions in duplicated effort.

The Three Enterprise Problems Nobody Talks About

1. No Authoritative Inventory
Imagine discovering:

14 agents querying the same CRM
8 different PDF summarizers
5 duplicate MCP servers

All created independently.
Without a registry, teams unknowingly rebuild capabilities that already exist.

The outcome:

Duplicate costs
Version drift
Increased maintenance burden

2.No Cross-Team Discovery

Often the best agent in the company sits unused.

Not because it is poor.

Because nobody knows it exists.

A reusable KYC agent built by one team could help five other teams.

Without discovery capabilities, engineers keep reinventing the wheel.

AWS Agent Registry introduces a searchable catalog designed to solve this problem.

3.No Governance or Audit Trail
When an AI agent makes a critical business decision, leadership asks:

Who created it?
Which version executed?
Was it security reviewed?
What permissions did it have?

Without a registry, answering these questions becomes painful.

AWS addresses this through:

Ownership tracking
Lifecycle management
Access control
Approval workflows
Auditing capabilities

What Exactly Is AWS Agent Registry?
At its core, AWS Agent Registry is a centralized management layer for:

AI Agents
MCP Servers
Agent Skills
Tools
Custom Resources

Instead of treating these capabilities as isolated assets, the registry transforms them into discoverable enterprise resources.

Think of it as:

A service catalog for enterprise AI.

Understanding the Two-Plane Architecture
One of the most intuitive ways to understand Agent Registry is through its two-plane model.

┌───────────────────────────────────────────┐
│            AWS AGENT REGISTRY             │
└───────────────────────────────────────────┘
                    │
      ┌─────────────┴─────────────┐
      │                           │
      ▼                           ▼

┌──────────────────┐   ┌──────────────────┐
│ GOVERNANCE PLANE │   │ DISCOVERY PLANE  │
├──────────────────┤   ├──────────────────┤
│ Register Agents  │   │ Semantic Search  │
│ Ownership        │   │ Lexical Search   │
│ Compliance       │   │ Recommendations  │
│ Access Policies  │   │ Discovery        │
│ Versions         │   │ Reuse            │
│ Audit Trails     │   │ Productivity     │
└────────┬─────────┘   └────────┬─────────┘
         │                      │
         └──────────┬───────────┘
                    ▼

      ┌────────────────────────────┐
      │ Enterprise Agent Catalog   │
      │                            │
      │ • Agents                   │
      │ • MCP Servers              │
      │ • Skills                   │
      │ • Tools                    │
      │ • Resources                │
      └────────────────────────────┘

                    ▼

      Discover • Reuse • Govern

Governance Plane

Think of the Governance Plane as a control tower.

It answers questions such as:

Who owns this agent?
Is it approved?
Which protocol does it use?
Which version is deployed?
What access should users have? Governance creates trust before consumption.

Without governance, a catalog is simply a list.

With governance, it becomes an enterprise platform.

Discovery Plane
Think of the Discovery Plane as Google for enterprise agents.

Instead of searching the internet, developers search internal capabilities.

Examples:

Customer onboarding agent

SharePoint MCP server

Transaction anomaly detection

The registry helps teams find reusable capabilities instead of rebuilding them.

Discovery transforms agents from isolated solutions into shared organizational assets.

How a Typical Enterprise Uses Agent Registry

Step 1: Build
A fraud-detection team creates a Bedrock-based investigation agent.

Step 2: Register

Metadata is published:

Name
Description
Owner
Compliance status
Access permissions
Invocation method

Step 3: Govern
Security and platform teams review:

Identity
Policies
Permissions
Documentation

Step 4: Discover
Another team searches:

transaction anomaly detection

The previously registered agent appears.

Step 5: Reuse

Instead of building agent #2, the team consumes agent #1.

This is where the real ROI appears.

Why This Matters for MCP

If you have been exploring MCP (Model Context Protocol), you will quickly discover a new challenge.

Organizations may eventually have hundreds of MCP servers.

Examples:

GitHub MCP
Jira MCP
ServiceNow MCP
Confluence MCP
SAP MCP
Internal custom MCPs

Without a catalog, developers do not know:

Which MCPs exist
Which are approved
Which should be used

Agent Registry effectively becomes the enterprise directory service for MCP ecosystems.

The Hidden Superpower: Shadow Agent Detection

One particularly interesting capability highlighted by AWS is the ability to automatically detect agents and MCP servers running across AgentCore environments.

Why does this matter?

Because every large enterprise eventually accumulates:

Forgotten prototypes
Unsupported agents
Unowned MCP servers
Experimental tools

These become:

Security risks
Compliance risks
Operational risks A registry helps bring these hidden assets into the light.

Think of it as moving from Shadow IT to Shadow Agents.

Key Takeaways

✅ Building agents is becoming easy.

✅ Managing hundreds of agents is becoming hard.

✅ Enterprises need discovery, governance, ownership, and auditability.

✅ AWS Agent Registry provides a centralized catalog for agents, tools, skills, MCP servers, and custom resources.

✅ The two-plane architecture (Governance + Discovery) is the most important concept to understand.

✅ The real value is not creating more agents.

✅ The real value is enabling organizations to reuse trusted agents at scale.

Final Thought

The first era of Agentic AI was about building agents.

The second era is about orchestrating agents.

The third era, which is beginning now, is about governing and discovering agents across the enterprise.

AWS Agent Registry is not merely another AI service.

It is an attempt to become the system of record for enterprise AI capabilities.

Just as app stores became the distribution layer for software, and service registries became foundational to microservices, Agent Registry could become foundational to enterprise Agentic AI.

The organizations that master discovery, governance, and reuse may ultimately create more value than those that simply build the most agents.

Right Model, Right Time: Why Model Routing Is Becoming Core to GenAI Platforms

Dee.Bee — Thu, 14 May 2026 09:52:18 +0000

Dee.Bee

May 14

Right Model, Right Time - Why Model Routing Is Becoming Core to GenAI Platforms

#ai #llm #architecture #microsoft

3 min read

Right Model, Right Time - Why Model Routing Is Becoming Core to GenAI Platforms

Dee.Bee — Thu, 14 May 2026 09:48:30 +0000

Why a single AI model is no longer enough

If you’re building AI-powered applications today, you’ve probably faced this problem already:

Some prompts are trivial, but they still hit an expensive model
Others are complex and fail badly when routed to a cheaper one
Latency, cost, and quality constantly pull in different directions

Using one model for every prompt is increasingly inefficient especially as new models with very different strengths are released every few weeks.

This is where model routing comes in.

The hospital triage analogy

Consider a large hospital where patients arrive all day with very different problems:

A sore throat
Chest pain
Sudden vision issues like floaters

Patients do not reliably know where to go. Some choose to see a consultant directly, some underplay their symptoms, while others bounce between departments losing valuable time in the process.

To handle this situation, hospitals rely on a triage lead.

The triage lead does not treat patients; they simply ensure that each patient is redirected to the right department in the hospital based on:

Complexity of the case – simple symptom vs. unclear combination of issues
Urgency / latency of the case – how quickly the case needs attention
Cost and resource use – does this really require top‑tier expertise now?
Expected performance / accuracy – does this require a specialist or advanced diagnosis?

What is model router

A model router is like that triage lead in a hospital. It intelligently routes prompts to the most suitable AI model from a collection of available models in real time rather than relying on a single model for all queries.

Simple prompts can be handled by smaller, faster, and cheaper models, while more complex reasoning can be routed to more capable models automatically.

How does a model router work?

The obvious next question that comes to mind is: how does a model router actually work?

Nowadays, models are released rapidly, and we already have benchmark datasets to compare them. It is hard to imagine that a single model will perform best across every dataset, especially when benchmarks measure very different capabilities.

As an AI developer or architect, the real interest is knowing which model performs best for a specific task or use case.

A model router is trained on various benchmark datasets to learn the relationship between prompt types and model strengths.

Large Language Model Routing with Benchmark Datasets

Model routing in cloud platforms

Cloud providers already offer out-of-the-box model routing capabilities:

Azure AI Foundry – Model Router
AWS Bedrock – Intelligent Prompt Router

According to Microsoft documentation:

We train the router on a large, diverse dataset spanning hundreds of thousands of examples across many domains. These include question answering, code generation, mathematical reasoning. Summarization, conversations, and agentic workflows are also covered. We continuously expand the training data to keep pace with new models and capabilities.

Model Router – How it works

Open ecosystem support

OpenRouter also provides similar capabilities. It allows AI engineers to optimise model usage based on specific needs such as cost, quality, or speed, while automatically maintaining fallback strategies.

Benefits of using a model router

Based on the incoming prompt, a model router intelligently identifies and routes to the most suitable model. Smaller, less expensive models are used when they are sufficient for the task.

This leads to:

Lower inference costs
Reduced latency
More efficient and sustainable compute usage

The following Microsoft community blog shares the cost benefits achieved using model routing in Azure AI Foundry:

Optimising AI costs with Microsoft Foundry Model Router

Measurable cost savings across all modes:

4.5% in Balanced, 4.7% in Cost, and 14.2% in Quality mode.

Quality mode saved the most by routing simple prompts to faster, cheaper models while still directing complex requests to more capable models.

What’s next?

In the next article, I’ll provide a step-by-step guide on how a model router can be created in Microsoft AI Foundry, covering model selection strategies, routing behaviour, and practical considerations for production systems.

Will ESB gradually die.......in era of microservices

Dee.Bee — Mon, 08 Apr 2024 10:36:13 +0000

Traditional integration aimed to solve the problem of data exchange between isolated applications. An Enterprise Service Bus (ESB) was a common tool used in this approach. It served as a centralized hub, allowing different applications to access and exchange data. However, this approach had its limitations, such as complexity in managing the ESB, lack of real-time data exchange, and difficulties in integrating with modern, cloud-based applications. This led to the evolution of new integration approaches like API-led connectivity and microservices architecture. These newer methods offer more flexibility, scalability, and efficiency in integrating diverse applications.

In the modern era, applications are moving away from monolithic architectures towards more flexible and scalable microservices architectures. These architectures break down application functionality into small, independent services, eliminating the need for a centralized data transfer point like an ESB. Instead, services communicate with each other in a decentralized manner, allowing for elastic scalability - the ability to easily add or remove service functionality as needed.

This architectural shift aligns well with agile development practices. Agile development breaks down the application development process into short, iterative sprints, each focused on delivering a complete set of functionality for a specific set of tasks. This approach naturally complements a microservices architecture, as microservices are small, discrete, and have clearly defined functions and service boundaries. This combination allows for rapid, iterative development and deployment, and facilitates continuous integration and continuous delivery (CI/CD) practices.

In conclusion, the shift towards microservices and agile development is a response to the need for more flexible, scalable, and efficient application development and deployment in today’s fast-paced digital world.

Traditional Integration:

Imagine a world where different software applications are like isolated islands. Each island has its own data, rules, and way of doing things.
Traditional integration aimed to connect these islands. It was like building bridges or tunnels between them so they could share information.
One common approach was using an Enterprise Service Bus (ESB). Think of the ESB as a central hub where data from different applications could meet and chat.
However, this often led to big, complex applications with tightly woven connections. It was like gluing puzzle pieces together to made a giant picture.
Integration was seen as part of the infrastructure—the behind-the-scenes plumbing that made everything work.

Modern Applications and Microservices:

Fast forward to today. We’re moving away from those giant puzzle pieces.
Instead of building monolithic skyscrapers, we’re creating smaller, independent buildings called microservices.
Each microservice has a specific job, like a tiny superhero with a unique power. They don’t need a central hub; they can talk directly to each other.
Imagine a city where these microservices are scattered around. They communicate flexibly, like neighbours borrowing sugar from each other.
Agile development fits right in. It’s like building one room at a time, adding features step by step. Each sprint is a mini construction project.
Microservices play well with this approach because they’re small, focused, and have clear boundaries. It’s like having separate rooms for cooking, sleeping, and playing.