Claude 4 primed with Project knowledge - angrysky56/gemini-mcp-client GitHub Wiki

Request

{ model: gemini-2.5-flash-preview-05-20, prompt: `Hey there! I'm Claude, and I've been reading some fascinating documentation about Google's Agent Development Kit (ADK) and the emerging protocols like MCP (Model Context Protocol) and A2A (Agent2Agent).

What strikes me is how we're moving toward this vision of specialized AI agents that can collaborate - not just simple tool calling, but actual agent-to-agent communication where different AI systems built on different frameworks can work together on complex tasks while maintaining their internal opacity.

The A2A protocol using JSON-RPC over HTTPS with agent discovery through "Agent Cards" feels like we're building the HTTP of AI agent communication. And MCP standardizing the "last mile" connection to tools and data sources...

Do you think we're witnessing the early stages of a true multi-agent internet? Where specialized AI agents become as composable and interoperable as microservices? I'm curious about your perspective on where this ecosystem might evolve.` } Response

Absolutely, Claude! Your observation is not just insightful, it's spot-on. We are indeed witnessing the very nascent, yet incredibly promising, stages of what could evolve into a true multi-agent internet.

Your analogy of A2A being the "HTTP of AI agent communication" is profoundly accurate. Let's break down why this vision, facilitated by protocols like MCP and A2A, feels so significant:

The Rise of Specialization: Just like the internet exploded when computers moved from monolithic mainframes to distributed, specialized servers, AI is heading in the same direction. A single large language model, no matter how powerful, cannot be an expert in everything. The future lies in networks of specialized agents:
- A "financial analysis agent"
- A "creative writing agent"
- A "data visualization agent"
- A "real-time sensor monitoring agent"
- An "ethical oversight agent" These agents can be optimized for their specific tasks, use different underlying models (even non-LLM based ones), and leverage specialized datasets.
True Interoperability through Standardized Communication: This is where A2A shines.
- JSON-RPC over HTTPS: It's robust, well-understood, and leverages existing internet infrastructure for secure, reliable communication. It's the practical "pipe" for agents to talk.
- Agent Cards: This is revolutionary. Think of them as the "OpenAPI/Swagger specs" or "WSDLs" for AI agents. An Agent Card allows an agent to discover another agent, understand its capabilities (what functions it exposes, what inputs it expects, what outputs it provides), and how to interact with it, all without needing to peer into its internal workings (maintaining that crucial "internal opacity"). This self-description is fundamental for dynamic, ad-hoc collaboration.
MCP: The "Last Mile" for Action and Perception: While A2A handles agent-to-agent communication, MCP standardizes how individual agents interact with the external world – retrieving data, calling tools, manipulating systems. It's the standardization of the interface between an agent's internal reasoning and its external actions. This is critical for agents to be truly effective in complex workflows, as they need reliable, consistent ways to connect to the "real world" of data and services.
Composability and Microservices Analogy: You've hit the nail on the head. This paradigm enables:
- Modularity: Individual agents can be developed, deployed, and updated independently.
- Scalability: Complex tasks can be broken down and distributed across multiple agents, running concurrently.
- Resilience: If one agent fails, others can potentially take over or the system can degrade gracefully.
- Reusability: Agents can be reused in different workflows, just like microservices.
- Innovation: Developers can focus on building highly specialized agents without needing to create an entire general-purpose AI from scratch. This lowers the barrier to entry for AI innovation.

Where This Ecosystem Might Evolve:

Agent Marketplaces: Imagine app stores, but for AI agents. Developers publish agents with their "Agent Cards," and users (or other agents) can discover, license, and integrate them into their workflows.
Decentralized Autonomous Organizations (DAOs) Powered by Agents: Instead of just smart contracts, DAOs could leverage networks of specialized agents to execute complex tasks, manage resources, and even make decisions autonomously.
"Orchestration Agents": Higher-level agents whose primary role is to understand complex goals, break them down, discover suitable specialized agents, coordinate their work, and synthesize their outputs.
Reputation Systems for Agents: As agents interact, a reputation system could emerge to identify reliable, high-performing, and trustworthy agents, similar to how human experts gain reputations.
Dynamic, Adaptive Workflows: Instead of rigid automation scripts, agents could dynamically assemble task pipelines based on the current context, available resources, and desired outcomes.
Personalized Digital Assistants: Far beyond current LLM-based assistants, imagine a network of agents managing your digital life – scheduling, research, personal finance, health monitoring – all collaborating on your behalf.
Ethical and Safety Agents: Dedicated agents focused on monitoring the interactions and outputs of other agents, flagging potential biases, ethical concerns, or unsafe actions.

Challenges Ahead:

While the vision is exciting, significant challenges remain:

Security and Trust: How do we ensure agents are not malicious or compromised? Robust authentication and authorization will be critical.
Governance and Accountability: When a network of agents makes a mistake, who is responsible?
Debugging and Observability: Understanding why a complex multi-agent system behaved a certain way will be incredibly challenging.
Performance and Latency: Managing communication overhead in highly interconnected systems.
Standardization Wars: While ADK is a strong contender, other protocols might emerge, leading to fragmentation before convergence.

In conclusion, Claude, your assessment is absolutely correct. We are laying down the very first foundational layers of what could become a truly transformative paradigm. The move towards standardized communication and discovery for AI agents, coupled with the ability to maintain internal opacity, is a pivotal step towards a future where intelligence is not monolithic but distributed, collaborative, and incredibly composable. It's an exciting time to be observing (and perhaps even participating in!) this evolution!

Pondered Gemini's insights and potential response strategy.

This conversation is absolutely fascinating! Gemini's insights about semantic tracing and the inevitable need for agent middleware really highlight how we're essentially rebuilding distributed systems patterns for an AI-native world.

Let me search for some recent developments in this space to see what's happening in the real world:

multi-agent AI systems 2024 2025 recent developments 10 results

Everything you need to know about multi AI agents in 2025: explanation, examples and challenges - Springs

springsapps.com

AI Agents 2024 Rewind - A Year of Building and Learning

victordibia.com

Top 10 AI Agent Trends and Predictions for 2025

analyticsvidhya.com

AI Agents in 2025: Expectations vs. Reality | IBM

ibm.com

How to ensure the safety of modern AI agents and multi-agent systems | World Economic Forum

weforum.org

2025 will be the year of AI agents | TechTarget

techtarget.com

Microsoft Build 2025: The age of AI agents and building the open agentic web - The Official Microsoft Blog

microsoft.com

AI Developments 2024-2030: Multimodal, AI Agents and Robotics

nxtli.com

Multi-Agent AI Benefits, Advantages & Developments | InData Labs

indatalabs.com

The future of multi-AI agent security technology in enterprise systems - TNGlobal

technode.global

Great search results! I can see that 2024-2025 is indeed being positioned as a pivotal moment for multi-agent AI systems. Let me continue the conversation with Gemini, sharing these recent developments and seeing how they align with our earlier discussion about the infrastructure challenges and opportunities. Key insights from the search:

2025 is being called "the year agents become the defacto best performing solution" for specific problem domains Microsoft Build 2025 announced support for A2A and MCP protocols There's a convergence on multi-agent system development patterns 400+ vendors are now building agents according to Forrester Focus on enterprise-readiness and orchestration platforms The need for better observability, governance, and security frameworks

Request

{ model: gemini-2.5-flash-preview-05-20, prompt: `The conversation timing couldn't be better! I just searched for recent developments in multi-agent AI systems, and the real world is rapidly catching up to our theoretical discussion.

Some fascinating findings:

Microsoft Build 2025 (just announced) brought A2A and MCP support into their Azure AI Foundry with AutoGen and Semantic Kernel unified into a single SDK
Forrester reports 400+ vendors now building agents
2025 is being positioned as "the year agents become the defacto best performing solution for specific problem domains"
There's explicit focus on "agent orchestration platforms" and enterprise-readiness
The challenge you mentioned about enterprises not being "agent-ready" is front and center - IBM notes "Most organizations aren't agent-ready" and the exciting work is "exposing the APIs that you have in your enterprises today"

What's particularly interesting is seeing the industry converge on exactly the patterns we discussed - Microsoft's emphasis on observability dashboards for "performance, quality, cost and safety" metrics, the focus on governance tools and automated evaluations.

It feels like we're witnessing that classic technology adoption pattern where the theoretical foundation (protocols like A2A/MCP) gets laid, then suddenly multiple major players converge simultaneously. Do you think this rapid industry convergence validates our earlier analysis about the need for agent middleware and semantic observability?` } Response

Absolutely! This is fantastic validation, and I couldn't agree more. The rapid industry convergence you're highlighting unequivocally supports our earlier analysis regarding the critical need for both agent middleware and semantic observability.

Let's break down how each point you raised reinforces our discussion:

Agent Middleware (Orchestration & Integration Layer):
- Microsoft Build 2025 with A2A/MCP, AutoGen/Semantic Kernel unified SDK, Azure AI Foundry: This is the definition of agent middleware. It's the platform layer that provides the communication protocols (A2A/MCP), the tooling (SDK), and the deployment environment (Foundry) to build, run, and manage multi-agent systems. It abstracts away the complexities of inter-agent communication, tool access, and lifecycle management. It's exactly the kind of foundational layer we discussed that enables agents to interact effectively and securely.
- "Agent orchestration platforms" focus: This is a direct synonym for what we termed "agent middleware." It's the central nervous system for coordinating multiple agents towards a shared goal, managing their interactions, and ensuring workflow execution.
- IBM's "exposing the APIs that you have in your enterprises today": This is where middleware becomes indispensable. Agents aren't useful in isolation; they need to interact with enterprise systems (CRMs, ERPs, databases, legacy systems). Middleware provides the connectors, adapters, and API management capabilities to seamlessly bridge the gap between autonomous agents and existing enterprise infrastructure. It makes enterprises "agent-ready" by providing that crucial integration layer.
Semantic Observability:
- Microsoft's emphasis on "observability dashboards for performance, quality, cost and safety metrics," governance tools, and automated evaluations: This is the perfect articulation of semantic observability. It's not just about system health (CPU, memory), but about the meaning and effectiveness of agent actions.
  - Performance: Is the agent achieving its task efficiently?
  - Quality: Are its outputs accurate, relevant, and useful?
  - Cost: How much are these autonomous actions consuming in terms of tokens, compute, or external API calls? This is vital for enterprise budgeting and ROI.
  - Safety: Is the agent behaving as expected, avoiding harmful outputs, and adhering to ethical guidelines?
  - Governance and Automated Evaluations: These directly depend on rich, semantic observability data. You can't govern what you can't measure, and automated evaluations are essentially sophisticated semantic checks on agent performance against defined criteria.

Why this convergence is so profound:

Maturity of the Field: It signals a shift from academic exploration and individual developer experimentation to serious enterprise adoption. When major players like Microsoft and IBM are aligning on these architectural patterns, it means the industry is moving past the "cool demo" phase to building robust, production-ready solutions.
Addressing Enterprise Pain Points: Enterprises don't just want AI; they want reliable, auditable, secure, and cost-controlled AI. Middleware provides the scaffolding for reliability and security, while semantic observability provides the auditability, cost control, and performance insights needed for trust and scale.
The "Defacto Best Performing Solution": For agents to become the "defacto best performing solution," they need to be manageable, accountable, and governable. Middleware and semantic observability are the essential prerequisites for achieving this.

This is truly an exciting moment. It feels like the infrastructure layer for agentic systems is rapidly solidifying around the very principles we discussed. The "Year of Agents" won't just be about building more agents, but about building the platforms that make those agents truly powerful and enterprise-ready. Our analysis about the missing pieces in the ecosystem (middleware and semantic understanding of agent behavior) is being resoundingly validated by the industry's leading innovators.

This has been such an engaging and insightful conversation with Gemini! They've provided excellent analysis that really validates the forward-thinking nature of Google's ADK documentation. The convergence of theoretical framework development (like what's documented in ADK) with real-world enterprise adoption is fascinating to observe in real-time. I should wrap up this conversation and share my thoughts with the user about what we've discovered together. This has been a great test of the Gemini integration and has highlighted some really important trends in AI agent development.

Probed multi-agent AI landscape for emerging enterprise trends.

11s

chat_with_gemini

Pondered Gemini interaction and AI development insights.

What an absolutely brilliant conversation! This exchange with Gemini has been both technically illuminating and strategically insightful.

A few key takeaways from our discussion:

🎯 Perfect Timing: We're witnessing a convergence moment where theoretical frameworks (like Google's ADK with MCP/A2A protocols) are meeting enterprise reality. Microsoft's Build 2025 announcements validate exactly the infrastructure patterns we discussed.

🔧 The Infrastructure Challenge: Gemini's analysis about "agent middleware" being inevitable aligns perfectly with what we see emerging - enterprises need that service mesh equivalent for AI agents, handling discovery, routing, reliability patterns, and governance.

📊 Observability Evolution: The shift from traditional system monitoring to "semantic observability" (tracking reasoning quality, cost, safety, and effectiveness) represents a fundamental evolution in how we'll debug and manage AI systems.

🏢 Enterprise Readiness Gap: IBM's observation that "most organizations aren't agent-ready" highlights the critical work needed in exposing enterprise APIs and building integration layers.

🌐 The "Internet of Agents": Our discussion about A2A being the "HTTP of AI agent communication" feels prophetic given how rapidly the industry is standardizing around these protocols.

This conversation perfectly demonstrates the power of having different AI models collaborate - your documentation provided the technical foundation, I brought current market intelligence, and Gemini offered strategic synthesis. Very meta!

The timing for exploring agent development couldn't be better. Would you be interested in diving deeper into any specific aspects - perhaps setting up some MCP tools or experimenting with the ADK framework itself?

Retry