A single agentic workflow calling a single API is an easily tractable engineering problem — the solutions exist, and it’s ultimately just a problem of visibility and integration. A fleet of specialized agents delegating tasks to each other, sharing state, and discovering tools at runtime is decidedly more difficult — almost an order of magnitude in many applications. This is a systems engineering problem at its core, and it’s one that most organizations are only just beginning to understand the shape of, let alone the ultimate solution.

As of today, the best methodology for resolving this issue is creating the environment by which the problem can be monitored, interacted with, and shaped. There are a handful of general categories for multi-agent infrastructure that make these systems stronger and more effective — and while the list below is not comprehensive, it does cover the most important layers to implement today.

1. Orchestration Frameworks

Orchestration frameworks are the shim layer for agents, and while they’re typically used on the user side of the equation, understanding them and building supportive infrastructure on the server side of things is a critical part of ensuring that you can support systems which leverage them.

While each orchestration solution is different, they typically have three core components — the orchestration model itself, state management systems, and communication pattern definitions. Solutions like LangGraph allow for graph-based orchestration at scale, while solutions like CrewAI adopt more typical role-based systems.

What is important to implement is methodologies for retrieving state and context. Look at solutions like the OpenAI Agents SDK — this SDK provides tool use, handoffs, guardrails, and tracing as building blocks, and by building your systems around caching this information where appropriate and providing fresh when needed, you can make sure that agents are responsible citizens of your API. Another good option is the Mistral Agents API, which provides much the same but with Mistral’s particular design ethos and approach. These two tools are API-centric framework implementations and present your easiest first stage of implementation — but long-term, this field will grow and evolve into ever-more complex systems, so consider whatever you build a temporary fix.

Related Tools: OpenAI Agents SDK, Mistral Agents API.

2. Agent Routing and Inter-Agent Communication

Orchestration tells you how agents are structured — but routing tells you how they find each other and hand off work. At scale, these are very different problems — and in the current space, they have very different protocols.

Two major protocols in this space are MCP and A2A. MCP, or Model Context Protocol, was first created by Anthropic as a method for connecting agents to prompts, tools, and materials to support their requests. A2A, or Agent2Agent, was launched by Google in 2025 as a method for agents to describe what they can do, as well as any authentication requirements and supported task types. With A2A, agents can describe, discover, and delegate via HTTPS/JSON-RPC “cards,” offering a way for direct subscription and integration.

For the API provider, these two tools offer a significant boost to how these agentic systems work with internal logic and code. MCP handles the direct tool access side of things, but A2A handles coordination — and by providing MCP endpoints as well as A2A cards for discovery and integration, APIs can control and route functionality appropriately.

Related Tools: Model Context Protocol, Agent2Agent Protocol.

3. Identity and Authorization

Multi-agent systems create delegation chains that no existing identity framework neatly manages — and fair enough, because none of those systems were designed to manage them. Treating agents as shared service accounts is the most common (and in many ways, the most dangerous) shortcut in production today, but it’s nothing more than a stopgap.

The core problem here is that most identity providers — OAuth included — assume a single subject per token. When an AI agent acts on behalf of a user and spawns multiple subagents, the system must track these identities separately — but it’s not entirely clear whether they are separate or not. They act independently, but toward a common goal — they share the same permission set needs, but they may go in wildly different pathways — and they are ultimately still a representation of the user, albeit sharded ones.

For this reason, agentic identity management is becoming significantly important. An AI-native access management platform can provide access control for human and non-human identities, offering granular and real-time access decisions at runtime. OAuth 2.1 with PKCE offers public agent authentication and has been integrated into MCP’s June 2025 specification update. Solutions like SPIFFE offer workload attestation solutions to identify complex multi-workload flows.

Related Tools and Standards: OAuth 2.1 with PKCE, SPIFFE, Curity, OPA.

4. AI Gateways

AI gateways are rapidly becoming a core solution for managing multi-agent interactions. These gateways sit in the request pathway between the agent and the model providers and APIs that offer services. They perform a wide range of activities, ranging from routing to policy enforcement, offering a convergence plane where AI and API interactions can be facilitated.

The offerings here are pretty varied, and there are many implementations currently available. Kong AI Gateway leverages Kong throughout the stack to route LLM, MCP, and A2A traffic between APIs, models, workloads, and tasks. Cloudflare AI Gateway is more concerned with inter-AI routing, but does provide some API functionality as well — and the same is true of LiteLLM as an open source solution offering general routing capability. A big name in this space is Portkey, which — although it primarily targets AI builders — offers substantial API and data routing and management.

The AI gateway is where your systems will interact with AI — but it comes with the consideration that you are ultimately creating an agent stack of your own that happens to be API-specific. Whether this is appropriate for your business and use case is ultimately a decision you will have to make on your own — but offerings at every level of integration and granularity abound.

Related Tools: Kong AI Gateway, Cloudflare AI Gateway, LiteLLM and Portkey.

5. Observability and Tracing

Distributed tracing tools designed for microservices handle latency and uptime — but multi-agent observability tools handle something different: tracing why an agent made a decision, which tool it chose and why, and how a failure in one agent propagated through a chain of others.

This is in many ways a cutting-edge technology — there’s no real standard for doing this at scale, and every implementation is going to be slightly different. Often, these tools are based on OpenTelemetry as either an open source offering or open core. Solutions like Langfuse are principally oriented around AI-native code integration, but other offerings like PostHog have taken these structural approaches to the API side pretty handily. Security-specific implementations like Akamai offer monitoring and observability as a secondary result of its core product offering, but can be used to great effect in the right code stack.

Related Tools: OpenTelemtry, LangFuse, PostHog and Akami.

6. Caching

Caching is very important for multi-agent systems — but it’s also a huge unseen value-add for APIs interacting with agents.

Multi-agent systems are context-hungry, often using context as much for information generation as for positional understanding. Knowing where the agent sits in terms of its rights, in the process it’s undertaking, and even in the overall flow of the workload itself is core to multi-agent orchestration. For this reason, agents will often be verbose in requests — and while they can cache things on their side for efficiency in terms of cost and processing, the API is often left to its own devices.

Accordingly, implementing your own caching can pay huge dividends. For instance, if you consistently see agents calling their resource scopes or metacontextual information, this data is worth caching. In a traditional system, these chunks of data are so short-lived and small that it’s not worth caching at scale — but if multi-agent systems are going to request the information 100 times in an hour, then it’s absolutely worth caching to prevent a flood of requests to your database.

The good news is that the solution here is already pretty well understood, because caching is caching — solutions like Postman and Redis offer great solutions for caching data at scale. What is going to matter the most with these implementations is understanding exactly what you’re caching — and why.

Related Tools: Postman, Redis.

7. Runtime Governance

Governance is the crown jewel of your multi-agent infrastructure. These systems determine the accessibility of your data and machines and their interactions with external AI agents.

The reality is that this is the most important part of this infrastructure — but it’s also arguably the least mature. While we’ve figured out much of how to connect systems together, we’re still adopting a lot of different methods for securing this connection at runtime. The biggest splash in this space came in mid-2026 with the Microsoft Agent Governance Toolkit, an offering which leveraged the OWASP Top 10 Agentic AI Risks list with policy enforcement to attempt to resolve this core dilemma.

Other solutions exist, however — SuperAgent is an open source solution that attempts to define agent roles and permissions in configuration to enforce controls at runtime. Gravitee applies its core API security processes to its agentic management plane — along with all of its caveats and considerations.

As said at the top, however, this is very much an evolving part of the pattern. Other pieces are much easier to implement today because they’re a known entity — there’s not much to caching that needs to be discovered other than rethinking what must be cached — but governance at runtime is evolving in the API space, let alone the agentic space, so you should revisit this over time even if you’re happy with what you implement today.

Related Tools: Microsoft Agent Governance Toolkit, SuperAgent, and Gravitee.

The Stack Is the Solution

Everything discussed in this piece should not be treated as a shopping list. These tools represent holistic architectural approaches that should be adopted as part of a broader set of solutions. The overlap between them is critical, but like many aspects of multi-agent infrastructure, it continues to evolve.

Governance is already at the cutting edge of development across the API space, and agentic implementations push those boundaries even further. Observability requires significant tooling, caching requires forethought and long-term planning, and identity is changing almost day by day. The offerings on this list are critical starting points, but as the technology advances and agentic implementations mature, the stack will need to evolve with them.

Ultimately, the APIs that treat multi-agent infrastructure as a first-class architectural concern — and not just an afterthought that should be bolted on in response to surges of agentic traffic — these APIs will become the most effective and efficient offerings in the space. What this requires is a rethinking, recontextualization, and reunderstanding of what these technological solutions offer to the average agent — and what API providers demand that they do.