The Architect’s Dilemma – O’Reilly

The agentic AI panorama is exploding. Each new framework, demo, and announcement guarantees to let your AI assistant e-book flights, question databases, and handle calendars. This speedy development of capabilities is thrilling for customers, however for the architects and engineers constructing these techniques, it poses a elementary query: When ought to a brand new functionality be a easy, predictable software (uncovered through the Mannequin Context Protocol, MCP) and when ought to it’s a classy, collaborative agent (uncovered through the Agent2Agent Protocol, A2A)?

The frequent recommendation is commonly round and unhelpful: “Use MCP for instruments and A2A for brokers.” That is like telling a traveler that automobiles use motorways and trains use tracks, with out providing any steerage on which is best for a selected journey. This lack of a transparent psychological mannequin results in architectural guesswork. Groups construct advanced conversational interfaces for duties that demand inflexible predictability, or they expose inflexible APIs to customers who desperately want steerage. The result is commonly the identical: a system that appears nice in demos however falls aside in the true world.

On this article, I argue that the reply isn’t discovered by analyzing your service’s inside logic or know-how stack. It’s discovered by trying outward and asking a single, elementary query: Who is looking your product/service? By reframing the issue this fashion—as a consumer expertise problem first and a technical one second—the architect’s dilemma evaporates.

This essay attracts a line the place it issues for architects: the road between MCP instruments and A2A brokers. I’ll introduce a transparent framework, constructed across the “Merchandising Machine Versus Concierge” mannequin, that can assist you select the correct interface based mostly in your client’s wants. I may even discover failure modes, testing, and the highly effective Gatekeeper Sample that exhibits how these two interfaces can work collectively to create techniques that aren’t simply intelligent however really dependable.

Two Very Completely different Interfaces

MCP presents instruments—named operations with declared inputs and outputs. The caller (an individual, program, or agent) should already know what it needs, and supply an entire payload. The software validates, executes as soon as, and returns a outcome. In case your psychological picture is a merchandising machine—insert a well-formed request, get a deterministic response—you’re shut sufficient.

A2A presents brokers—goal-first collaborators that converse, plan, and act throughout turns. The caller expresses an end result (“e-book a refundable flight below $450”), not an argument listing. The agent asks clarifying questions, calls instruments as wanted, and holds onto session state till the job is finished. In case you image a concierge—interacting, negotiating trade-offs, and sometimes escalating—you’re in the correct neighborhood.

Neither interface is “higher.” They’re optimized for various conditions:

• MCP is quick to cause about, simple to check, and robust on determinism and auditability.
• A2A is constructed for ambiguity, long-running processes, and desire seize.

Bringing the Interfaces to Life: A Reserving Instance

To see the distinction in observe, let’s think about a easy job: reserving a selected assembly room in an workplace.

The MCP “merchandising machine” expects a superbly structured, machine-readable request for its book_room_tool. The caller should present all essential info in a single, legitimate payload:

{
  "jsonrpc": "2.0",
  "id": 42,
  "methodology": "instruments/name",
  "params": {
    "identify": "book_room_tool",
    "arguments": {
      "room_id": "CR-104B",
      "start_time": "2025-11-05T14:00:00Z",
      "end_time": "2025-11-05T15:00:00Z",
      "organizer": "consumer@instance.com"
    }
  }
}

Any deviation—a lacking discipline or incorrect knowledge kind—ends in a right away error. That is the merchandising machine: You present the precise code of the merchandise you need (e.g., “D4”) otherwise you get nothing.

The A2A “concierge,“ an “workplace assistant” agent, is approached with a high-level, ambiguous objective. It makes use of dialog to resolve ambiguity:

Person: “Hey, are you able to e-book a room for my 1-on-1 with Alex tomorrow afternoon?”
Agent: “In fact. To ensure I get the correct one, what time works finest, and the way lengthy will you want it for?”

The agent’s job is to take the ambiguous objective, collect the required particulars, after which possible name the MCP software behind the scenes as soon as it has an entire, legitimate set of arguments.

With this clear dichotomy established—the predictable merchandising machine (MCP) versus the stateful concierge (A2A)—how will we select? As I argued within the introduction, the reply isn’t present in your tech stack. It’s discovered by asking crucial architectural query of all: Who is looking your service?

Step 1: Determine your client

1. The machine client: A necessity for predictability
   Is your service going to be known as by one other automated system, a script, or one other agent performing in a purely deterministic capability? This client requires absolute predictability. It wants a inflexible, unambiguous contract that may be scripted and relied upon to behave the identical approach each single time. It can not deal with a clarifying query or an sudden replace; any deviation from the strict contract is a failure. This client doesn’t desire a dialog; it wants a merchandising machine. This nonnegotiable requirement for a predictable, stateless, and transactional interface factors on to designing your service as a software (MCP).
2. The human (or agentic) client: A necessity for comfort
   Is your service being constructed for a human finish consumer or for a classy AI that’s making an attempt to satisfy a posh, high-level objective? This client values comfort and the offloading of cognitive load. They don’t need to specify each step of a course of; they need to delegate possession of a objective and belief that it is going to be dealt with. They’re comfy with ambiguity as a result of they count on the service—the agent—to resolve it on their behalf. This client doesn’t need to comply with a inflexible script; they want a concierge. This requirement for a stateful, goal-oriented, and conversational interface factors on to designing your service as an agent (A2A).

By beginning with the buyer, the architect’s dilemma typically evaporates. Earlier than you ever debate statefulness or determinism, you first outline the consumer expertise you’re obligated to supply. Usually, figuring out your buyer gives you your definitive reply.

Step 2: Validate with the 4 components

Upon getting recognized who calls your service, you have got a robust speculation to your design. A machine client factors to a software; a human or agentic client factors to an agent. The following step is to validate this speculation with a technical litmus check. This framework offers you the vocabulary to justify your selection and make sure the underlying structure matches the consumer expertise you propose to create.

1. Determinism versus ambiguity
   Does your service require a exact, unambiguous enter, or is it designed to interpret and resolve ambiguous targets? A merchandising machine is deterministic. Its API is inflexible: GET /merchandise/D4. Another request is an error. That is the world of MCP, the place a strict schema ensures predictable interactions. A concierge handles ambiguity. “Discover me a pleasant place for dinner” is a legitimate request that the agent is anticipated to make clear and execute. That is the world of A2A, the place a conversational circulation permits for clarification and negotiation.
2. Easy execution versus advanced course of
   Is the interplay a single, one-shot execution, or a long-running, multistep course of? A merchandising machine performs a short-lived execution. All the operation—from fee to shelling out—is an atomic transaction that’s over in seconds. This aligns with the synchronous-style, one-shot mannequin of MCP. A concierge manages a course of. Reserving a full journey itinerary would possibly take hours and even days, with a number of updates alongside the best way. This requires the asynchronous, stateful nature of A2A, which may deal with long-running duties gracefully.
3. Stateless versus stateful
   Does every request stand alone or does the service want to recollect the context of earlier interactions? A merchandising machine is stateless. It doesn’t do not forget that you got a sweet bar 5 minutes in the past. Every transaction is a clean slate. MCP is designed for these self-contained, stateless calls. A concierge is stateful. It remembers your preferences, the main points of your ongoing request, and the historical past of your dialog. A2A is constructed for this, utilizing ideas like a session or thread ID to take care of context.
4. Direct management versus delegated possession
   Is the buyer orchestrating each step, or are they delegating your complete objective? When utilizing a merchandising machine, the buyer is in direct management. You’re the orchestrator, deciding which button to press and when. With MCP, the calling software retains full management, making a sequence of exact operate calls to realize its personal objective. With a concierge, you delegate possession. You hand over the high-level objective and belief the agent to handle the main points. That is the core mannequin of A2A, the place the buyer offloads the cognitive load and trusts the agent to ship the result.

Desk 1: 4 query framework

These components aren’t unbiased checkboxes; they’re 4 sides of the identical core precept. A service that’s deterministic, transactional, stateless, and immediately managed is a software. A service that handles ambiguity, manages a course of, maintains state, and takes possession is an agent. By utilizing this framework, you’ll be able to confidently validate that the technical structure of your service aligns completely with the wants of your buyer.

No framework, regardless of how clear…

…can completely seize the messiness of the true world. Whereas the “Merchandising Machine Versus Concierge” mannequin supplies a strong information, architects will finally encounter companies that appear to blur the strains. The bottom line is to recollect the core precept we’ve established: The selection is dictated by the buyer’s expertise, not the service’s inside complexity.

Let’s discover two frequent edge instances.

The advanced software: The iceberg
Contemplate a service that performs a extremely advanced, multistep inside course of, like a video transcoding API. A client sends a video file and a desired output format. This can be a easy, predictable request. However internally, this one name would possibly kick off a large, long-running workflow involving a number of machines, high quality checks, and encoding steps. It’s a vastly advanced course of.

Nevertheless, from the buyer’s perspective, none of that issues. They made a single, stateless, fire-and-forget name. They don’t have to handle the method; they only want a predictable outcome. This service is like an iceberg: 90% of its complexity is hidden beneath the floor. However as a result of its exterior contract is that of a merchandising machine—a easy, deterministic, one-shot transaction—it’s, and ought to be, carried out as a software (MCP).

The straightforward agent: The scripted dialog
Now think about the alternative: a service with quite simple inside logic that also requires a conversational interface. Think about a chatbot for reserving a dentist appointment. The inner logic is perhaps a easy state machine: ask for a date, then a time, then a affected person identify. It’s not “clever” or significantly versatile.

Nevertheless, it should keep in mind the consumer’s earlier solutions to finish the reserving. It’s an inherently stateful, multiturn interplay. The buyer can not present all of the required info in a single, prevalidated name. They should be guided by the method. Regardless of its inside simplicity, the necessity for a stateful dialogue makes it a concierge. It should be carried out as an agent (A2A) as a result of its consumer-facing expertise is that of a dialog, nevertheless scripted.

These grey areas reinforce the framework’s central lesson. Don’t get distracted by what your service does internally. Give attention to the expertise it supplies externally. That contract along with your buyer is the final word arbiter within the architect’s dilemma.

Testing What Issues: Completely different Methods for Completely different Interfaces

A service’s interface doesn’t simply dictate its design; it dictates the way you validate its correctness. Merchandising machines and concierges have basically totally different failure modes and require totally different testing methods.

Testing MCP instruments (merchandising machines):

• Contract testing: Validate that inputs and outputs strictly adhere to the outlined schema.
• Idempotency assessments: Make sure that calling the software a number of instances with the identical inputs produces the identical outcome with out unwanted side effects.
• Deterministic logic assessments: Use commonplace unit and integration assessments with fastened inputs and anticipated outputs.
• Adversarial fuzzing: Take a look at for safety vulnerabilities by offering malformed or sudden arguments.

Testing A2A brokers (concierges):

• Purpose completion fee (GCR): Measure the proportion of conversations the place the agent efficiently achieved the consumer’s high-level objective.
• Conversational effectivity: Observe the variety of turns or clarifications required to finish a job.
• Software choice accuracy: For advanced brokers, confirm that the correct MCP software was chosen for a given consumer request.
• Dialog replay testing: Use logs of actual consumer interactions as a regression suite to make sure updates don’t break present conversational flows.

The Gatekeeper Sample

Our journey to this point has centered on a dichotomy: MCP or A2A, merchandising machine or concierge. However probably the most refined and sturdy agentic techniques don’t drive a selection. As an alternative, they acknowledge that these two protocols don’t compete with one another; they complement one another. The final word energy lies in utilizing them collectively, with every enjoying to its strengths.

The simplest strategy to obtain that is by a robust architectural selection we are able to name the Gatekeeper Sample.

On this sample, a single, stateful A2A agent acts as the first, user-facing entry level—the concierge. Behind this gatekeeper sits a set of discrete, stateless MCP instruments—the merchandising machines. The A2A agent takes on the advanced, messy work of understanding a high-level objective, managing the dialog, and sustaining state. It then acts as an clever orchestrator, making exact, one-shot calls to the suitable MCP instruments to execute particular duties.

Contemplate a journey agent. A consumer interacts with it through A2A, giving it a high-level objective: “Plan a enterprise journey to London for subsequent week.”

• The journey agent (A2A) accepts this ambiguous request and begins a dialog to collect particulars (actual dates, finances, and so on.).
• As soon as it has the required info, it calls a flight_search_tool (MCP) with exact arguments like origin, vacation spot, and date.
• It then calls a hotel_booking_tool (MCP) with the required metropolis, check_in_date, and room_type.
• Lastly, it’d name a currency_converter_tool (MCP) to supply expense estimates.

Every software is an easy, dependable, and stateless merchandising machine. The A2A agent is the good concierge that is aware of which buttons to press and in what order. This sample supplies a number of important architectural advantages:

• Decoupling: It separates the advanced, conversational logic (the “how”) from the straightforward, reusable enterprise logic (the “what”). The instruments could be developed, examined, and maintained independently.
• Centralized governance: The A2A gatekeeper is the right place to implement cross-cutting issues. It may possibly deal with authentication, implement fee limits, handle consumer quotas, and log all exercise earlier than a single software is ever invoked.
• Simplified software design: As a result of the instruments are simply easy MCP capabilities, they don’t want to fret about state or conversational context. Their job is to do one factor and do it properly, making them extremely sturdy.

Making the Gatekeeper Manufacturing-Prepared

Past its design advantages, the Gatekeeper Sample is the best place to implement the operational guardrails required to run a dependable agentic system in manufacturing.

• Observability: Every A2A dialog generates a novel hint ID. This ID should be propagated to each downstream MCP software name, permitting you to hint a single consumer request throughout your complete system. Structured logs for software inputs and outputs (with PII redacted) are important for debugging.
• Guardrails and safety: The A2A Gatekeeper acts as a single level of enforcement for important insurance policies. It handles authentication and authorization for the consumer, enforces fee limits and utilization quotas, and may keep a listing of which instruments a selected consumer or group is allowed to name.
• Resilience and fallbacks: The Gatekeeper should gracefully handle failure. When it calls an MCP software, it ought to implement patterns like timeouts, retries with exponential backoff, and circuit breakers. Critically, it’s accountable for the ultimate failure state—escalating to a human within the loop for overview or clearly speaking the problem to the tip consumer.

The Gatekeeper Sample is the final word synthesis of our framework. It makes use of A2A for what it does finest—managing a stateful, goal-oriented course of—and MCP for what it was designed for—the dependable, deterministic execution of a job.

Conclusion

We started this journey with a easy however irritating downside: the architect’s dilemma. Confronted with the round recommendation that “MCP is for instruments and A2A is for brokers,” we have been left in the identical place as a traveler making an attempt to get to Edinburgh—realizing that automobiles use motorways and trains use tracks however with no instinct on which to decide on for our particular journey.

The objective was to construct that instinct. We did this not by accepting summary labels, however by reasoning from first rules. We dissected the protocols themselves, revealing how their core mechanics inevitably result in two distinct service profiles: the predictable, one-shot “merchandising machine” and the stateful, conversational “concierge.”

With that basis, we established a transparent, two-step framework for a assured design selection:

1. Begin along with your buyer. Probably the most important query isn’t a technical one however an experiential one. A machine client wants the predictability of a merchandising machine (MCP). A human or agentic client wants the comfort of a concierge (A2A).
2. Validate with the 4 components. Use the litmus check of determinism, course of, state, and possession to technically justify and solidify your selection.

In the end, probably the most sturdy techniques will synthesize each, utilizing the Gatekeeper Sample to mix the strengths of a user-facing A2A agent with a collection of dependable MCP instruments.

The selection is now not a dilemma. By specializing in the buyer’s wants and understanding the basic nature of the protocols, architects can transfer from confusion to confidence, designing agentic ecosystems that aren’t simply useful but additionally intuitive, scalable, and maintainable.