Building the Agentic AI Landscape
Components, Vendors, and the backbone that connects them.
There is a moment in every technology shift when the pieces start to become clear. Not just what the technology does, but what it requires, what infrastructure, what governance, what connective tissue makes it real rather than theoretical. We are in that moment now with agentic AI. The rise of agentic AI is reshaping our understanding of autonomous systems in ways we are only beginning to comprehend.
By leveraging agentic AI, businesses can enhance decision-making processes and improve customer experiences, marking a transition to a future where human and machine collaboration is seamless. The potential of agentic AI is vast, offering new opportunities for innovation and efficiency within enterprises. As organizations integrate agentic AI into their operations, they must also consider the ethical implications of deploying such technologies.
Enterprises are no longer asking whether AI agents are real. They are asking how to build the landscape that makes agents trustworthy, scalable, and governable. That is a harder question, and it is the one this article attempts to answer.
Each component is vital for the successful implementation of agentic AI, providing the structure necessary for advanced computational tasks. The agentic AI landscape is not a single product. It is a system of twelve interdependent components, each essential, each served by a distinct category of technology. Understanding which component does what, and which vendors operate in each space, is the foundation of any credible AI strategy.
In the sections below I will walk you through each of those twelve components, what they do, which technology vendors operate in that space, and how those vendors differ from one another. And because sequence matters as much as selection, I will close with a practical recommendation on where to start, so you build the right foundation before you build the house.
The Twelve Components of an Agentic AI Landscape
Think of the agentic AI landscape as a jigsaw puzzle. Each piece is a distinct component with a specific role. No piece is optional. Remove one and the picture has a gap. What makes this puzzle particularly interesting is that one piece sits at the centre, connecting all the others. That piece is the real-time event backbone, and the vendor that purpose-built this infrastructure for enterprise AI is Solace.
The twelve components are: the access layer, identity and trust, integration and connectivity, the real-time event mesh, agentic AI orchestration, agent registry and discovery, the LLM layer, model runtime and RAG infrastructure, the data and memory layer, the tool and action layer, guardrails and governance, and monitoring and observability. Each one serves a different purpose. Each one is served by a different set of vendors. And each one connects to all the others through the event backbone.
The landscape of agentic AI is being defined by vendors who are innovating continuously to serve the unique needs of businesses.
1. The Access Layer: Your Intelligent Front Door
Every agentic AI system needs a front door, and in 2026 that front door needs to be intelligent. The access layer is where external requests, human users, and machine-to-machine communications enter the agentic AI landscape. In the agentic AI era, this means far more than routing HTTP traffic. API gateways must now support the Model Context Protocol (MCP), the emerging standard that allows agents to discover and invoke tools and services dynamically. They must enforce rate limiting, manage authentication for non-human callers, and act as the first line of defense against malicious inputs or agent misbehavior. Ensuring these functions are robust is critical for the successful deployment of agentic AI systems.
Four vendors define this space today, each with a distinct character.
Kong
The broadest story in this category. Kong has moved aggressively into the agentic stack, launching a dedicated MCP Gateway in version 3.12 and an Agent Gateway for agent-to-agent traffic in version 3.14. For organizations that want a single vendor governing API traffic, LLM calls, MCP tool access, and agent coordination simultaneously, Kong is currently the most complete option.
WSO2
Where Kong adds AI capabilities as plugins, WSO2 has designed its AI gateway from first principles. It generates MCP servers automatically from existing OpenAPI specifications, provides an MCP Hub for tool discovery, and ships with semantic caching built in. WSO2 is the strongest choice for organizations where AI-native architecture design matters more than breadth of ecosystem.
Gravitee
Purpose-built for event-native architectures. Gravitee offers first-class support for asynchronous APIs including WebSocket, Webhook, SSE, and event streams, alongside REST. The natural fit when the rest of your architecture is already event-driven. It adds AI identity and access management features for agent authentication.
MuleSoft
The enterprise standard for organizations running the full Salesforce and Anypoint stack. MuleSoft integrates API management into its broader iPaaS suite, making it the right choice when API governance needs to sit alongside heavy integration work. Its AI gateway features lag the purpose-built alternatives, but for MuleSoft-committed enterprises the consolidation benefit often outweighs that gap.
2. Identity and Trust: The Existential Question of the Agentic Era
In a world where software agents act autonomously on behalf of humans and organizations, the question of identity becomes existential. Which agent is making this request? Is it authorized? Has it been tampered with? The identity and trust component addresses the full lifecycle of agent authentication and authorization, extending familiar human identity frameworks to non-human machine identities.
Zero-trust architecture, where no agent is trusted by default and every interaction is verified, becomes the baseline expectation. This is not theoretical. Research published in late 2025 found that non-human identities now outnumber human users by eighty-two to one in the average enterprise. The identity layer is the accountability layer, and without it, the autonomy of AI agents is a liability rather than a feature.
Microsoft Entra
The most complete enterprise agent identity story today. Entra Agent ID, announced at Microsoft Build 2025, automatically issues a directory identity to every agent created in Copilot Studio or Azure AI Foundry, enabling Conditional Access policies and a full audit trail. The natural default for Microsoft-committed organizations.
Okta
The leading independent identity platform, Okta, managing the credential lifecycle for agents as first-class non-human identities. Strong for multi-cloud and vendor-neutral environments where Microsoft ecosystem lock-in is undesirable.
WSO2
Bundles its identity server natively with its API manager, making it the most integrated single-vendor option for organizations that want API governance and agent identity managed together without separate licensing.
3. Integration and Connectivity: The Tool Fabric for Agents
Agents are only as useful as the systems they can reach. The integration and connectivity layer provides the bridge between the AI landscape and the vast ecosystem of enterprise applications, databases, and external services that agents need to read from and write to. iPaaS platforms that enterprises have relied on for years become, in the agentic context, the tool fabric, translating agent intent into concrete actions across ERP systems, CRMs, supply chain platforms, and beyond. Without this layer, agents operate in isolation. With it, they become genuinely capable participants in enterprise processes.
Boomi
The longest-standing leader in the Gartner Magic Quadrant for iPaaS, recognized in every edition since the category’s inception. Boomi has pivoted hard to agentic AI with Agentstudio, which provides agent lifecycle management through an Agent Garden, Agent Designer, Agent Control Tower, and Agent Marketplace. Native MCP support and a growing set of acquisitions covering API management, data movement, and managed file transfer make Boomi the most complete low-code integration option for enterprises moving into agent-driven architectures.
MuleSoft
The standard for large, complex enterprises with dedicated integration teams and deep API-led architectures. MuleSoft’s strength is abstraction and governance at scale, and it carries the weight of Salesforce’s enterprise ecosystem. Premium pricing and consumption-based cost structures can make it expensive at high volumes, but for the right organization the depth of capability justifies the investment.
WSO2
The open-source flexibility option. WSO2 Integrate offers deep customization, standards compliance, and no licensing lock-in, making it the preferred choice for technically capable teams that want to own their integration stack without depending on a proprietary vendor’s roadmap.
4. The Real-Time Event Mesh: The Backbone of Everything
Most enterprise systems were built to answer questions on demand. Agentic AI needs something fundamentally different: it needs to know when things happen, in real time, at scale, across distributed environments that span cloud, on-premises, and edge. The real-time event mesh is the nervous system of the agentic landscape, delivering streaming data to agents the moment state changes anywhere in the enterprise. Coordinated workflows powered by agentic AI can lead to significant improvements in productivity and innovation.
“An agent that polls is slow, resource-intensive, and structurally reactive. An agent that receives events via the Solace event mesh is responsive, efficient, and capable of acting on change the instant it occurs. This is not a performance optimization. It is the architectural foundation that makes real-time agentic AI possible.“
Ruben van der Zwan, 2026
Solace’s real-time data movement platform, the Solace event mesh, provides this infrastructure as a fully managed, protocol-agnostic backbone that connects agents to data streams regardless of where those streams originate. The event mesh supports multiple protocols simultaneously, routes events using smart hierarchical topic structures, and moves data across cloud, on-premises, and edge environments without requiring agents to know where their data lives.
What is Solace Agent Mesh? Solace Agent Mesh is an agent development and runtime platform — natively supports ADLC, cheaper and faster to run, open and vendor neutral. You can try it your self with this easy Desktop App. Or read more on Solace’s Agent Mesh web page.
The distinction between polling and event-driven matters enormously at enterprise scale. Polling creates unnecessary load, introduces latency between events and responses, and scales poorly as agent populations grow. Event-driven architecture eliminates all three problems at once. Agents subscribe to the events they care about and receive them the instant they occur. That responsiveness is not a feature of the agent framework. It is a property of the backbone.
It is worth noting that SAP Integration Suite’s Advanced Event Mesh is built on and powered by Solace PubSub+. For organizations already in the SAP ecosystem, the path to an event-driven agentic backbone is shorter than it might appear.
5. Agentic AI Orchestration: Coordination becomes Intelligence
Orchestration is where the intelligence of a multi-agent system becomes visible. Individual agents are capable, but coordinated agents are transformative. The orchestration layer manages how agents are spawned, how they delegate tasks to one another, how they communicate asynchronously across distributed environments, and how their outputs are composed into coherent, auditable outcomes. The orchestration of agentic AI systems brings a new level of operational efficiency.
Solace Agent Mesh
Purpose-built for enterprise-grade multi-agent coordination. Built on open standards including A2A and MCP, Solace Agent Mesh is engineered for the governance, resilience, and operational requirements that enterprise deployments demand. Unlike lightweight developer frameworks, it is designed as infrastructure, not scaffolding. It connects agents built in any framework through A2A proxy, integrates with any LLM through the Solace AI Connector, and uses the event mesh as its communication backbone, giving every agent interaction the reliability and observability properties of enterprise messaging.
LangChain / LangGraph
The dominant developer-facing framework, with the broadest integration ecosystem in the market and the most mature MCP support. LangGraph v1.0 introduced a graph and state-machine model with durable checkpointing, time-travel debugging, and human-in-the-loop interrupts. For stateful, complex production orchestration, LangGraph is the current standard. Its paired observability platform, LangSmith, makes it a natural choice for teams that need end-to-end visibility into agent reasoning chains.
Microsoft Copilot Studio
The low-code agent builder for Microsoft-ecosystem teams. Copilot Studio brings agent creation to business users without requiring programming expertise, and connects those agents natively to Teams, SharePoint, Dynamics 365, and the broader Microsoft application landscape. The right choice for organizations where broad internal adoption matters more than technical depth.
CrewAI and AutoGen
CrewAI’s role-based crew model makes it the fastest path from concept to working multi-agent prototype. AutoGen, now superseded by Microsoft’s unified Agent Framework, excels at conversational multi-agent patterns and human-in-the-loop research workflows. Both are better suited to prototyping and focused use cases than to enterprise-wide production orchestration.
6. Agent Registry and Discovery: The Dynamic Directory
As the number of agents in an enterprise environment grows from dozens to hundreds, a foundational challenge emerges that is easy to underestimate: how does one agent find another? How does it know what capabilities are available, which tools can be invoked, and which agents are authorized to collaborate? The agent registry and discovery layer is the dynamic directory of the agentic landscape.
This is an emerging but architecturally critical layer. Without it, agent ecosystems become isolated silos, with individual agents unable to collaborate effectively or discover new capabilities as the environment evolves. Solace’s platform includes native capabilities in this space, leveraging the event mesh as the discovery backbone and providing the runtime infrastructure through which agents can register, advertise, and find each other dynamically. Agent Cards, published as events over the mesh, are the open-standard mechanism emerging as the interoperability baseline for agent discovery. The enterprises that build on open, standards-aligned infrastructure today will be the ones who avoid painful migrations tomorrow.
7. The LLM Layer: Swappable Reasoning Engines
The large language model is the reasoning engine at the heart of every AI agent. It processes inputs, interprets context, formulates responses, and decides on actions. The LLM layer is not monolithic: enterprises increasingly choose foundation models based on capability, cost, latency, and data sovereignty requirements.
The dominant pattern in 2026 is multi-model routing, using cheaper and faster models for simple tasks and frontier models for complex reasoning, to control cost and avoid lock-in. Models are increasingly becoming a swappable layer rather than a strategic moat. The strategic value has migrated upward to orchestration and downward to governed data and context.
OpenAI GPT-4o / GPT-5
The frontier capability benchmark and the most widely deployed commercial model family. Deep enterprise reach through ChatGPT Enterprise and Microsoft Azure OpenAI.
Anthropic Claude
The safety and governance leader, increasingly the standard in regulated industries including legal, healthcare, and financial services. Strong long-context reasoning and coding capabilities. Available through Anthropic’s API, AWS Bedrock, and Google Vertex AI.
Google Gemini
Native multimodality and long context, with deep integration across Google Cloud, Google Workspace, and Vertex AI. Balanced with the open-weight Gemma family for organizations that want model flexibility.
AWS Bedrock
Not a model but a managed multi-model platform providing access to Anthropic, Meta, Amazon, and other foundation models through a single governed API. The default for AWS-committed enterprises that want model flexibility without managing multiple vendor relationships.
8. Model Runtime and RAG Infrastructure: Grounded Intelligence
A foundation model alone is not sufficient for enterprise use. Agents need to retrieve relevant, up-to-date information from organizational knowledge bases, connect model reasoning to real enterprise data, and do so reliably and at scale. Retrieval-augmented generation, or RAG, is the architecture that makes this possible, combining vector search over enterprise documents and data with LLM generation to produce grounded, accurate responses.
Agentic AI requires adaptable and robust data layers to function optimally and utilize the full potential of all components.
AWS Bedrock, Azure AI Foundry, and Google Vertex AI provide managed runtime environments that combine model hosting, embedding pipelines, and retrieval infrastructure in governed, enterprise-grade packages. This layer is where the general capability of a foundation model becomes the specific intelligence of a domain-aware enterprise agent. The choice here almost always follows existing cloud commitments and compliance requirements, including data residency for European enterprises.
9. The Data and Memory Layer: What Agents Remember
Agents need memory. Not just the ephemeral context of a single conversation, but structured, persistent access to enterprise knowledge, historical interactions, and semantic understanding of organizational data. The data and memory layer encompasses the full range of storage technologies that give agents long-term context.
Pinecone
The fastest path to production for managed vector search. Fully managed and serverless, Pinecone is the default for teams prioritizing shipping speed. Its strength is simplicity and low operational overhead. The tradeoff is cost at high query volumes and limited control over recall tuning.
Weaviate
The native hybrid search option. Weaviate combines dense vector search with BM25 keyword retrieval in a single query, which matters when agents need exact matches on identifiers, version numbers, and proper nouns alongside semantic recall. Open-source with a managed cloud option, it scales to very large vector collections.
PGvector
The right default for the majority of enterprise agent workloads under approximately ten million vectors when the organization already runs PostgreSQL. It delivers full ACID compliance, native SQL joins, and no additional infrastructure. The pgvectorscale extension extends competitive performance to around fifty million vectors at materially lower cost than managed services.
Neo4j
The knowledge graph option, for when the relationships between data entities matter as much as the entities themselves. Neo4j allows agents to reason over complex organizational structures, hierarchies, and interconnections that flat vector search cannot represent.
Snowflake
The structured enterprise data platform, providing agents with governed access to analytical data at scale. The right choice when agents need to reason over historical records, aggregated metrics, and structured reporting data alongside unstructured document knowledge.
10. The Tool and Action Layer: Giving Agents Hands
Agents reason and plan, but ultimately they must act. The tool and action layer defines what agents can actually do in the world: call APIs, trigger robotic process automation, query databases, update records, initiate transactions. The richness of this layer determines the practical utility of an agentic system. An agent that can reason but not act is a consultant with no hands.
Boomi, MuleSoft, and WSO2 surface their integration capabilities here as agent-executable tools, with the same vendor considerations that apply in the integration layer. UiPath brings robotic process automation into scope, allowing agents to interact with legacy systems that lack modern APIs. Microsoft Copilot Studio extends this into the Microsoft application ecosystem, enabling agents to take actions across Teams, SharePoint, Dynamics, and beyond.
11. Guardrails, Governance, and Compliance
Autonomy without accountability is not a feature. It is a liability. The guardrails, governance, and compliance component is the layer that ensures agentic AI operates within defined boundaries, respects regulatory requirements, and produces outputs that meet organizational standards. This includes technical safety measures such as content filtering, prompt injection detection, and output validation, as well as broader governance capabilities including data residency controls, audit trail generation, and policy enforcement.
Guardrails AI and LangSmith provide developer-facing tools for implementing safety layers in agent workflows. AWS Bedrock Guardrails offers managed policy enforcement at the model runtime level, integrated into the same platform as the model itself. Solace contributes governance capabilities through its Event Portal, providing schema governance and audit trails for the event-driven and agentic infrastructure it manages. In an era of increasing AI regulation in Europe and beyond, this layer is the foundation of enterprise trust in autonomous systems.
12. Monitoring and Observability: Govern what you cannot see
The monitoring and observability layer provides end-to-end visibility across the entire agentic landscape: which agents are running, what decisions they are making, how long LLM calls are taking, where errors are occurring, and what the total cost of an agent workflow is. This visibility is qualitatively different from traditional application monitoring, because agent failures appear in multi-step causal chains rather than single failed requests. Understanding why an agent made a wrong decision requires tracing the full chain of reasoning, not just the last function call. The right tools can significantly enhance the effectiveness of agentic AI implementations.
Datadog and Dynatrace
Enterprise APM platforms that have extended their capabilities to cover AI-specific metrics and traces. The strongest choice for organizations that want unified observability across traditional infrastructure and agentic AI in a single platform, without adding a new tooling layer.
LangSmith
LLM-native observability, capturing the full chain of reasoning behind agent decisions with native LangChain and LangGraph integration, graph visualization, and checkpoint replay. The default for LangChain-centric teams that need deep visibility into agent reasoning, not just infrastructure metrics.
Langfuse
The leading open-source observability option, released under the MIT license and designed for self-hosting. The right choice for organizations with data residency requirements or a preference for self-managed infrastructure.
Solace
Contributes real-time observability across the event mesh and agent communication layer, giving platform and operations teams continuous visibility into how agents are communicating, where bottlenecks appear, and what the state of every event-driven interaction is at any given moment.
The Vendor Landscape at a Glance
Organizations that invest in agentic AI must prioritize a strong data foundation for effective performance. Successful agentic AI deployments hinge on comprehensive strategies that integrate all facets of the technology landscape.
Looking across the twelve components, a clear pattern emerges. Some vendors are specialists, owning a single component deeply. Others span multiple layers. Understanding these coverage patterns is the key to building a coherent stack without redundancy.
Solace is the only vendor that provides both a purpose-built enterprise event backbone and a multi-agent orchestration infrastructure built on open standards. It is not an API gateway, not an iPaaS, and not a vector database. It is designed to connect all of those, providing the real-time nervous system that every other component depends on to function at enterprise speed.
To thrive, enterprises must leverage the full capabilities of their agentic AI implementations through careful planning and execution. The future of enterprise operations will be increasingly shaped by the strategic use of agentic AI technologies.
Kong leads on breadth at the access layer, with the most complete story for governing API, LLM, MCP, and agent traffic from a single platform. WSO2 uniquely spans API management, integration, and identity in a single open-source stack, making it the most integrated single-vendor option for governance-focused organizations. Boomi remains the broadest low-code integration platform, now extending into agent lifecycle management with Agentstudio. The advancements in these tools are critical for supporting enterprises adopting agentic AI technology.
MuleSoft is the right choice for very large, complex enterprises with specialist integration teams and deep Salesforce ecosystem dependencies. Gravitee belongs in architectures that are already event-native. LangChain and LangGraph define the developer standard for orchestration at the framework level. The hyperscaler model platforms (AWS Bedrock, Azure AI Foundry, Google Vertex AI) follow cloud commitments and compliance requirements more than technical differentiation.
“The enterprise leaders who will define the next decade are not those who deploy the most impressive single AI model. They are those who build the most coherent, integrated, and governed agentic landscape, one where each piece fits precisely with the next.”
Ruben van der Zwan, 2026
Nice read: Agentic-AI: Astonishing AI movement forces System Integrators to rethink their role
Where to Start
Knowing the components is one thing. Knowing where to begin is another, and this is where most enterprise AI initiatives go wrong. Organizations invest heavily in LLMs, orchestration frameworks, and agent interfaces, and then discover that the underlying data infrastructure cannot support the real-time demands those agents place on it. The AI is capable. The foundation is not.
I have seen this pattern repeat across industries. A promising pilot that works in isolation, but stalls when it needs to reach real enterprise data in real time. The root cause is almost always the same. The data layer was not designed for the moment-to-moment demands of autonomous agents. It was designed for scheduled batch processes, periodic reports, and human-paced queries. Agents do not operate at human pace.
“You would not build a house without a foundation, and then try to add one afterwards. The same logic applies to agentic AI. Get your data moving in real time first. Build your integration layer with event-driven architecture at its core. Then layer intelligence on top.“
Ruben van der Zwan, 2026
My strong strategic recommendation for building an agentic AI solution, is to start with your data and integration layer, and to build it on an event-driven foundation. Before you select an orchestration framework, before you finalize your LLM strategy, before you design your agent workflows, answer one question: can your enterprise data move in real time to wherever it is needed? If the answer is no, everything built on top of it will underperform. Agents that cannot receive timely, accurate data will make poor decisions. RAG pipelines fed by stale data will produce unreliable answers. Governance frameworks applied to slow data flows will always be one step behind reality.
An event mesh gives you that foundation. It decouples your data producers from your data consumers, makes real-time data universally available across cloud, on-premises, and edge environments, and does so in a way that scales without rearchitecting. Every component in the agentic landscape benefits from having this in place first. Agents respond to events rather than poll for them. Integration flows become reactive rather than scheduled. Observability becomes continuous rather than periodic. Governance can be applied at the data layer before it ever reaches an agent.
None of the twelve components is optional. Remove the access layer and you have no controlled entry point. Remove identity and trust and you have no accountability. Remove the event mesh and you have no real-time awareness. Remove governance and you have autonomous systems operating without boundaries.
The agentic AI puzzle is in front of us. The pieces are becoming clear. The enterprises that will define the next decade are those who assemble them in the right order.
Interested in discussing this further? I’d be happy to connect.
