The Architect’s Dilemma – O’Reilly

The agentic AI panorama is exploding. Each new framework, demo, and announcement guarantees to let your AI assistant guide flights, question databases, and handle calendars. This fast development of capabilities is thrilling for customers, however for the architects and engineers constructing these programs, it poses a elementary query: When ought to a brand new functionality be a easy, predictable device (uncovered through the Mannequin Context Protocol, MCP) and when ought to it’s a complicated, 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 steering on which is best for a selected journey. This lack of a transparent psychological mannequin results in architectural guesswork. Groups construct complicated conversational interfaces for duties that demand inflexible predictability, or they expose inflexible APIs to customers who desperately want steering. The result is commonly the identical: a system that appears nice in demos however falls aside in the actual world.

On this article, I argue that the reply isn’t discovered by analyzing your service’s inner logic or know-how stack. It’s discovered by wanting 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 shopper’s wants. I can even discover failure modes, testing, and the highly effective Gatekeeper Sample that exhibits how these two interfaces can work collectively to create programs that aren’t simply intelligent however actually 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 desires, and supply a whole payload. The device validates, executes as soon as, and returns a consequence. 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 final result (“guide a refundable flight underneath $450”), not an argument listing. The agent asks clarifying questions, calls instruments as wanted, and holds onto session state till the job is completed. For those who 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 motive about, straightforward to check, and powerful 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 apply, let’s think about a easy activity: 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,
  "technique": "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 area or incorrect knowledge kind—ends in an instantaneous 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 purpose. It makes use of dialog to resolve ambiguity:

Consumer: “Hey, are you able to guide a room for my 1-on-1 with Alex tomorrow afternoon?”
Agent: “After all. To verify 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 purpose, collect the mandatory particulars, after which seemingly name the MCP device behind the scenes as soon as it has a whole, legitimate set of arguments.

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

Step 1: Determine your shopper

1. The machine shopper: A necessity for predictability
   Is your service going to be known as by one other automated system, a script, or one other agent appearing in a purely deterministic capability? This shopper requires absolute predictability. It wants a inflexible, unambiguous contract that may be scripted and relied upon to behave the identical means each single time. It can’t deal with a clarifying query or an surprising replace; any deviation from the strict contract is a failure. This shopper 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 device (MCP).
2. The human (or agentic) shopper: A necessity for comfort
   Is your service being constructed for a human finish consumer or for a complicated AI that’s attempting to meet a fancy, high-level purpose? This shopper 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 purpose and belief that will probably 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 shopper 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. Normally, figuring out your buyer provides you with your definitive reply.

Step 2: Validate with the 4 components

After getting recognized who calls your service, you may have a powerful speculation in your design. A machine shopper factors to a device; a human or agentic shopper factors to an agent. The subsequent step is to validate this speculation with a technical litmus take a look at. This framework offers you the vocabulary to justify your selection and make sure the underlying structure matches the consumer expertise you plan to create.

1. Determinism versus ambiguity
   Does your service require a exact, unambiguous enter, or is it designed to interpret and resolve ambiguous objectives? A merchandising machine is deterministic. Its API is inflexible: GET /merchandise/D4. Some other 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 stream permits for clarification and negotiation.
2. Easy execution versus complicated 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. The complete operation—from cost to allotting—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 way in which. This requires the asynchronous, stateful nature of A2A, which might 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 purchased 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 small print 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 keep up context.
4. Direct management versus delegated possession
   Is the buyer orchestrating each step, or are they delegating all the purpose? 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 utility retains full management, making a sequence of exact perform calls to realize its personal purpose. With a concierge, you delegate possession. You hand over the high-level purpose and belief the agent to handle the small print. 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 straight managed is a device. A service that handles ambiguity, manages a course of, maintains state, and takes possession is an agent. Through the use of this framework, you may confidently validate that the technical structure of your service aligns completely with the wants of your buyer.

No framework, irrespective of how clear…

…can completely seize the messiness of the actual world. Whereas the “Merchandising Machine Versus Concierge” mannequin gives a strong information, architects will finally encounter providers 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 inner complexity.

Let’s discover two frequent edge circumstances.

The complicated device: The iceberg
Take into account a service that performs a extremely complicated, multistep inner course of, like a video transcoding API. A shopper sends a video file and a desired output format. It is a easy, predictable request. However internally, this one name would possibly kick off an enormous, long-running workflow involving a number of machines, high quality checks, and encoding steps. It’s a massively complicated 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 consequence. 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 needs to be, applied as a device (MCP).

The easy agent: The scripted dialog
Now take into account the other: a service with quite simple inner logic that also requires a conversational interface. Think about a chatbot for reserving a dentist appointment. The interior logic may be 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 bear in mind the consumer’s earlier solutions to finish the reserving. It’s an inherently stateful, multiturn interplay. The buyer can’t present all of the required info in a single, prevalidated name. They must be guided by means of the method. Regardless of its inner simplicity, the necessity for a stateful dialogue makes it a concierge. It have to be applied 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. Deal with the expertise it gives externally. That contract together 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 completely different failure modes and require completely different testing methods.

Testing MCP instruments (merchandising machines):

• Contract testing: Validate that inputs and outputs strictly adhere to the outlined schema.
• Idempotency exams: Make sure that calling the device a number of occasions with the identical inputs produces the identical consequence with out unintended effects.
• Deterministic logic exams: Use customary unit and integration exams with mounted inputs and anticipated outputs.
• Adversarial fuzzing: Take a look at for safety vulnerabilities by offering malformed or surprising arguments.

Testing A2A brokers (concierges):

• Purpose completion price (GCR): Measure the proportion of conversations the place the agent efficiently achieved the consumer’s high-level purpose.
• Conversational effectivity: Observe the variety of turns or clarifications required to finish a activity.
• Device choice accuracy: For complicated brokers, confirm that the correct MCP device 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 current conversational flows.

The Gatekeeper Sample

Our journey to date has centered on a dichotomy: MCP or A2A, merchandising machine or concierge. However probably the most refined and sturdy agentic programs don’t power 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 method to obtain that is by means of a robust architectural selection we will 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 group of discrete, stateless MCP instruments—the merchandising machines. The A2A agent takes on the complicated, messy work of understanding a high-level purpose, 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.

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

• The journey agent (A2A) accepts this ambiguous request and begins a dialog to collect particulars (precise dates, finances, and so forth.).
• As soon as it has the mandatory 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 device 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 gives a number of important architectural advantages:

• Decoupling: It separates the complicated, conversational logic (the “how”) from the easy, reusable enterprise logic (the “what”). The instruments may be developed, examined, and maintained independently.
• Centralized governance: The A2A gatekeeper is the proper place to implement cross-cutting considerations. It may possibly deal with authentication, implement price limits, handle consumer quotas, and log all exercise earlier than a single device is ever invoked.
• Simplified device 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 nicely, 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 singular hint ID. This ID have to be propagated to each downstream MCP device name, permitting you to hint a single consumer request throughout all the system. Structured logs for device 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 price limits and utilization quotas, and may preserve an inventory of which instruments a specific consumer or group is allowed to name.
• Resilience and fallbacks: The Gatekeeper should gracefully handle failure. When it calls an MCP device, it ought to implement patterns like timeouts, retries with exponential backoff, and circuit breakers. Critically, it’s chargeable for the ultimate failure state—escalating to a human within the loop for assessment 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 activity.

Conclusion

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

The purpose was to construct that instinct. We did this not by accepting summary labels, however by reasoning from first ideas. 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 together with your buyer. Probably the most important query is just not a technical one however an experiential one. A machine shopper wants the predictability of a merchandising machine (MCP). A human or agentic shopper wants the comfort of a concierge (A2A).
2. Validate with the 4 components. Use the litmus take a look at of determinism, course of, state, and possession to technically justify and solidify your selection.

Finally, probably the most sturdy programs 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 elemental nature of the protocols, architects can transfer from confusion to confidence, designing agentic ecosystems that aren’t simply practical but in addition intuitive, scalable, and maintainable.