The Architect’s Dilemma – O’Reilly

The agentic AI panorama is exploding. Each new framework, demo, and announcement guarantees to let your AI assistant ebook flights, question databases, and handle calendars. This fast 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 device (uncovered by way of the Mannequin Context Protocol, MCP) and when ought to or not it’s a complicated, collaborative agent (uncovered by way of the Agent2Agent Protocol, A2A)?

The widespread recommendation is commonly round and unhelpful: “Use MCP for instruments and A2A for brokers.” That is like telling a traveler that vehicles use motorways and trains use tracks, with out providing any steering on which is healthier 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 steering. The end 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 inside 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 person 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 will help you select the appropriate 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 techniques 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 needs, and supply an entire payload. The device 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 final result (“ebook a refundable flight underneath $450”), not an argument checklist. The agent asks clarifying questions, calls instruments as wanted, and holds onto session state till the job is completed. Should you image a concierge—interacting, negotiating trade-offs, and infrequently escalating—you’re in the appropriate neighborhood.

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

• MCP is quick to purpose about, straightforward 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 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 obligatory data 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": "person@instance.com"
    }
  }
}

Any deviation—a lacking discipline or incorrect information kind—leads to a direct 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:

Person: “Hey, are you able to ebook a room for my 1-on-1 with Alex tomorrow afternoon?”
Agent: “After all. To verify I get the appropriate one, what time works greatest, 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 an entire, 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 crucial 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 sudden replace; any deviation from the strict contract is a failure. This shopper doesn’t need 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 person or for a complicated AI that’s making an attempt to satisfy 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 anticipate 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 patron, the architect’s dilemma typically evaporates. Earlier than you ever debate statefulness or determinism, you first outline the person expertise you’re obligated to offer. Generally, figuring out your buyer offers you your definitive reply.

Step 2: Validate with the 4 elements

After you have recognized who calls your service, you may have a robust speculation to your design. A machine shopper factors to a device; a human or agentic shopper factors to an agent. The following step is to validate this speculation with a technical litmus take a look at. This framework provides you the vocabulary to justify your alternative and make sure the underlying structure matches the person 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 sound 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 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. Your complete operation—from fee 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 best way. 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 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 patron orchestrating each step, or are they delegating all the purpose? When utilizing a merchandising machine, the patron 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 collection 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 main points. That is the core mannequin of A2A, the place the patron offloads the cognitive load and trusts the agent to ship the end result.

Desk 1: 4 query framework

These elements usually are not impartial checkboxes; they’re 4 sides of the identical core precept. A service that’s deterministic, transactional, stateless, and immediately managed is a device. 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 actual world. Whereas the “Merchandising Machine Versus Concierge” mannequin supplies a sturdy information, architects will ultimately encounter providers that appear to blur the traces. The secret is to recollect the core precept we’ve established: The selection is dictated by the patron’s expertise, not the service’s inside complexity.

Let’s discover two widespread edge circumstances.

The advanced device: The iceberg
Take into account a service that performs a extremely advanced, multistep inside 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 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 patron’s perspective, none of that issues. They made a single, stateless, fire-and-forget name. They don’t have to handle the method; they simply 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 needs to be, carried out as a device (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 interior logic could be a easy state machine: ask for a date, then a time, then a affected person identify. It’s not “clever” or notably versatile.

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

These grey areas reinforce the framework’s central lesson. Don’t get distracted by what your service does internally. Concentrate on 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 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 outcome with out unintended effects.
• Deterministic logic exams: Use commonplace unit and integration exams 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):

• Aim completion charge (GCR): Measure the proportion of conversations the place the agent efficiently achieved the person’s high-level purpose.
• Conversational effectivity: Observe the variety of turns or clarifications required to finish a activity.
• Instrument choice accuracy: For advanced brokers, confirm that the appropriate MCP device was chosen for a given person request.
• Dialog replay testing: Use logs of actual person interactions as a regression suite to make sure updates don’t break current conversational flows.

The Gatekeeper Sample

Our journey to date has targeted on a dichotomy: MCP or A2A, merchandising machine or concierge. However essentially the most refined and strong agentic techniques don’t power a alternative. 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 strong architectural alternative 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 advanced, 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 person interacts with it by way of 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 (actual dates, funds, and so on.).
• As soon as it has the mandatory data, 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 offer expense estimates.

Every device is a straightforward, dependable, and stateless merchandising machine. The A2A agent is the sensible 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 easy, reusable enterprise logic (the “what”). The instruments will be developed, examined, and maintained independently.
• Centralized governance: The A2A gatekeeper is the right place to implement cross-cutting considerations. It might probably deal with authentication, implement charge limits, handle person 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 features, 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 strong.

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 person 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 person, enforces charge limits and utilization quotas, and might preserve a listing of which instruments a selected person 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 answerable for the ultimate failure state—escalating to a human within the loop for assessment or clearly speaking the difficulty to the top person.

The Gatekeeper Sample is the final word synthesis of our framework. It makes use of A2A for what it does greatest—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 making an attempt to get to Edinburgh—realizing that vehicles 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 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 alternative:

1. Begin along 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 elements. Use the litmus take a look at of determinism, course of, state, and possession to technically justify and solidify your alternative.

In the end, essentially the most strong 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 not a dilemma. By specializing in the patron’s wants and understanding the elemental nature of the protocols, architects can transfer from confusion to confidence, designing agentic ecosystems that aren’t simply purposeful but in addition intuitive, scalable, and maintainable.