DEV Community: Alejandro Hernández

Agents Don't Start with Prompts. They Start with Events.

Alejandro Hernández — Thu, 13 Aug 2026 04:02:09 +0000

Lately, it seems like everything is an agent. Open LinkedIn and you'll
find posts about AI Agents. Go to YouTube and you'll see tutorials on
LangChain, OpenAI Agents, CrewAI, AutoGen, and a long list of frameworks
promising to build autonomous agents capable of performing complex
tasks.

After consuming enough of this content, it's easy to conclude that an
agent is simply an LLM connected to a set of tools. From my perspective,
that's the wrong way to think about it.

Most diagrams look something like this:

Prompt
  ↓
LLM
  ↓
Tool
  ↓
LLM
  ↓
Response

There's nothing inherently wrong with this model. In fact, it's
extremely useful for solving a wide range of problems. The problem
begins when we confuse one implementation pattern with the complete
definition of an agent.

To understand why, let's think about something millions of people do
every day: hiring.

When someone applies for a job, an important fact has occurred. Someone
submitted an application, and the recruiting process has begun. From
that moment on, many things can happen: the resume is analyzed, an
interview is scheduled, feedback is collected, experience is validated,
an offer is approved, and eventually a decision is made.

If we represented that process as events, it might look something like
this:

CandidateApplied
        ↓
ResumeAnalyzed
        ↓
InterviewScheduled
        ↓
InterviewCompleted
        ↓
FeedbackReceived
        ↓
OfferSent

Now let's ask an interesting question: where is the agent?

Under the current popular paradigm, we might say the agent is the model
that analyzed the resume. But that explanation is incomplete. Resume
analysis represents only a small part of the overall process. The
complete system observed new information, evaluated the current
situation, and executed actions to move the process forward.

In other words, the agent didn't appear when someone wrote a prompt.
The agent started acting when an event occurred.

This distinction may seem subtle, but it completely changes how we
design systems.

A recruiter doesn't wake up in the morning waiting for prompts. They
receive events. A candidate applies for a job. An interviewer submits
feedback. A manager approves a hire. A candidate rejects an offer. Each
new fact changes the state of the process and creates the need for new
decisions.

The same thing happens in almost every organization. A customer sends an
email. A payment is approved. A shipment changes status. A document is
uploaded. A support ticket is created.

The real world is made of events, not prompts.

Of course, an AI model may become involved at some point. It might
analyze a resume, classify an email, summarize a document, or determine
a customer's intent. It might even be an LLM. But regardless of the
technology being used, the agent is still the complete system. The model
participates in a decision; it doesn't define the agent by itself.

Imagine, for example, that a model analyzes a resume and determines that
the candidate meets the minimum requirements for the position. The
important output isn't the textual response generated by the model. The
important output is the new fact that was discovered:

ResumeAnalyzed
CandidateQualified

From that point, the system can continue operating. It can schedule an
interview, request additional information, or notify the recruiter. The
model participated in a decision, but the overall flow remains
event-driven.

And this is where I think a more useful definition of an agent begins to
emerge.

If we look at the complete process, the agent isn't constantly producing
responses. It's observing facts, evaluating information, and generating
new facts that allow the process to continue moving forward.

A candidate applies for a job.
That produces new information.
That information is evaluated.
The evaluation produces a new fact: the candidate qualifies for the position.
That new fact triggers new decisions.
An interview is scheduled.
Feedback is collected.
An offer is recommended.

Each decision generates new facts that feed the next step in the
process.

From this perspective, an agent stops looking like a conversation with
tools and starts looking like an event-driven decision-making
system.

Those decisions can be made using business rules, machine learning
models, LLMs, or a combination of them. The important thing isn't the
technology used to make the decision. The important thing is that there
is a system capable of observing what happens, evaluating the available
information, and reacting accordingly.

I think this is where the current conversation about agents becomes
confusing. We've spent enormous amounts of time talking about prompts,
tool calling, context windows, and reasoning, but relatively little time
talking about what causes an agent to act in the first place.

Because before a prompt exists, something has usually already happened:

A customer sent a message.
A document was uploaded.
A payment was approved.
An order was created.

The event is the trigger. The prompt, when one exists, is simply a
tool used during the evaluation process.

That's why I'm increasingly convinced that we've started this
conversation from the wrong end. Agents are much more than prompt
workflows connected to tools. They are systems that observe events, make
decisions, and execute actions.

Some of those decisions may be made by business rules. Others by machine
learning models. Others by LLMs. What matters is that the agent isn't
defined by the technology used to make a decision, but by its ability to
react to what happens.

Maybe we're not just beginning to build agents. Maybe we've been
building them for years, and we've simply added new tools that help them
make better decisions.

Prompts matter. LLMs matter too. But neither answers the fundamental
question:

What caused the agent to act?

Most of the time, the answer isn't a prompt. It's an event.

Agents don't start with prompts. They start with events.

Building Lightweight and Streamable MCP Servers on AWS Lambda with Python

Alejandro Hernández — Wed, 12 Aug 2026 23:53:24 +0000

MCP servers don't always need containers, persistent processes, or a full web framework.

For many tools, a regular Python AWS Lambda is enough:

MCP request
    ↓
API Gateway
    ↓
Lambda
    ↓
Python tool
    ↓
JSON-RPC response

But some operations are different.

A tool may spend 30 seconds searching, analyzing, or coordinating work and need to report progress while it runs.

For those workloads we need real Streamable HTTP:

tools/call
    ↓
progress
    ↓
progress
    ↓
progress
    ↓
final result

We wanted both models in Python without requiring applications to adopt different MCP programming models.

That led to two open-source projects:

modmex-lambda — the Python MCP runtime and programming model.
serverless-python-mcp — the Serverless Framework deployment integration.

Together they let a Python application start with a lightweight buffered MCP server and opt into real Lambda response streaming when the workload actually needs it.

Start with a regular Python Lambda

The simplest deployment doesn't require Lambda Web Adapter or response streaming.

Install modmex-lambda:

pip install modmex-lambda

Create an MCP server:

from modmex_lambda import APIGatewayHttpResolver
from modmex_lambda.mcp import MCPServer

mcp = MCPServer(
    name="orders",
    version="1.0.0",
)

Then expose capabilities as regular Python functions.

Tools

@mcp.tool()
def get_order(order_id: str) -> dict:
    return {
        "id": order_id,
        "status": "confirmed",
    }

Resources

@mcp.resource("orders://{order_id}")
def order_resource(order_id: str) -> dict:
    return load_order(order_id)

Prompts

@mcp.prompt()
def order_assistant(customer_id: str):
    return build_order_prompt(customer_id)

Mount the MCP server on the normal API Gateway resolver:

app = APIGatewayHttpResolver()

app.include_mcp(
    mcp,
    path="/mcp",
)

handler = app.handler

The resulting architecture is deliberately boring:

MCP Client
    ↓
API Gateway HTTP API v2
    ↓
AWS Lambda
    ↓
modmex-lambda
    ↓
MCPServer

For short-lived tools, resources, and prompts, that's usually exactly what we want.

No FastAPI.

No Flask.

No ASGI server.

No response-streaming infrastructure.

Just Python and Lambda.

MCP is another interface to the application layer

One of the design goals was not to create a separate application architecture for MCP.

Tools can use the same dependency injection mechanisms as regular Lambda endpoints:

@mcp.tool()
def get_order(
    order_id: str,
    service: Annotation[OrderService, Depends()],
):
    return service.get(order_id)

That means REST and MCP can remain thin interfaces over the same application services:

                 OrderService
                     ▲
                     │
          ┌──────────┴──────────┐
          │                     │
       REST API                MCP

The same idea applies to middleware.

Authorization, tenant resolution, logging, auditing, tracing, and policies don't need to be implemented inside every tool:

@mcp.tool(
    middlewares=[
        RequirePermission("orders:read"),
    ]
)
def get_order(...):
    ...

MCP becomes another transport into the application rather than another application architecture.

Why buffered MCP is useful

It's easy to associate MCP with streaming, but many MCP operations don't benefit from it.

Consider:

get_customer
get_order
calculate_route
lookup_inventory
read_resource
get_prompt

If a tool finishes in 300 milliseconds or two seconds, a normal Lambda response is simpler.

For this reason, streaming isn't a requirement in modmex-lambda.

You can run MCP through a regular managed Python Lambda and API Gateway HTTP API v2.

That gives us the first deployment model:

Python
+
modmex-lambda
+
Lambda
+
HTTP API v2

Then, when a workload actually needs incremental communication, we can move to the second model.

When buffered responses stop being enough

Consider a tool that performs several expensive steps:

@mcp.tool()
def analyze_market(ctx: MCPContext):
    opportunities = search_opportunities()
    ranked = rank_opportunities(opportunities)
    analysis = analyze_market_conditions(ranked)

    return build_recommendation(analysis)

Maybe the complete operation takes 30 or 40 seconds.

With a buffered response, the MCP client sees nothing until the function finishes.

Instead, we want the tool to report progress:

@mcp.tool()
def analyze_market(ctx: MCPContext):

    ctx.progress.report(
        1,
        total=4,
        message="Searching opportunities",
    )

    opportunities = search_opportunities()

    ctx.progress.report(
        2,
        total=4,
        message="Ranking candidates",
    )

    ranked = rank_opportunities(opportunities)

    ctx.progress.report(
        3,
        total=4,
        message="Analyzing market conditions",
    )

    analysis = analyze_market_conditions(ranked)

    ctx.progress.report(
        4,
        total=4,
        message="Building recommendation",
    )

    return build_recommendation(analysis)

Those progress reports are translated into MCP notifications/progress messages and sent over the same Streamable HTTP response before the final JSON-RPC result.

Now we need real response streaming.

Real MCP streaming from Python Lambda

For streaming, modmex-lambda provides LambdaWebAdapterResolver.

The application remains Python:

from modmex_lambda import LambdaWebAdapterResolver
from modmex_lambda.mcp import MCPServer

mcp = MCPServer(
    name="orders",
    version="1.0.0",
)

app = LambdaWebAdapterResolver()

app.include_mcp(
    mcp,
    path="/mcp",
)

handler = app.handler

The infrastructure changes underneath it:

MCP Client
    ↓
API Gateway REST API
    ↓
responseTransferMode = STREAM
    ↓
AWS Lambda
    ↓
Lambda Web Adapter
    ↓
Python application
    ↓
modmex-lambda

Lambda Web Adapter connects the HTTP response produced by the Python application with Lambda response streaming.

Now an MCP tool can emit progress while it is still running:

0s    tools/call
      ↓
4s    notifications/progress
      "Searching opportunities"
      ↓
12s   notifications/progress
      "Ranking candidates"
      ↓
21s   notifications/progress
      "Analyzing market conditions"
      ↓
30s   final JSON-RPC result

The Lambda invocation hasn't completed when those progress messages reach the MCP client.

That's real incremental MCP streaming.

Deployment is the second half of the problem

Getting streaming to work inside Python is only part of the job.

A streaming Lambda deployment also needs the right infrastructure:

Lambda Web Adapter
response streaming mode
API Gateway REST API
STREAM transfer mode
launcher configuration
architecture-specific adapter layer
packaging

We didn't want every Python MCP service to reproduce that configuration manually.

That's why we built serverless-python-mcp.

Install it:

npm install --save-dev serverless-python-mcp

and register it like any other Serverless Framework plugin:

plugins:
  - serverless-python-mcp

MCP servers are declared under custom.pythonMcp.servers.

Lightweight deployment with HTTP API v2

For a normal buffered MCP server:

custom:
  pythonMcp:
    servers:
      orders:
        handler: app.handler
        transport: httpApi
        streaming: false

The application uses:

app = APIGatewayHttpResolver()
app.include_mcp(mcp, path="/mcp")

handler = app.handler

The plugin creates a normal Lambda behind API Gateway HTTP API v2.

No Lambda Web Adapter is added.

No streaming launcher is added.

This remains the lightweight deployment path.

Streamable deployment with REST API

When the same class of application needs real streaming:

custom:
  pythonMcp:
    servers:
      orders:
        handler: app.handler
        transport: http
        streaming: true

The Python application switches to:

app = LambdaWebAdapterResolver()
app.include_mcp(mcp, path="/mcp")

handler = app.handler

The plugin takes care of the AWS-specific pieces required for streaming.

It attaches the architecture-specific Lambda Web Adapter layer, configures the execution wrapper and streaming mode, creates the launcher used by the HTTP process, and configures the REST API integration for streaming.

The developer still works with a Python MCP server.

Three deployment front doors

serverless-python-mcp currently supports three AWS front doors:

Transport	AWS front door	Buffered	Streaming
`httpApi`	API Gateway HTTP API v2	Yes	No
`http`	API Gateway REST API v1	Yes	Yes
`url`	Lambda Function URL	Yes	Yes

This lets the infrastructure match the workload.

For a simple internal MCP service:

transport: httpApi
streaming: false

For an MCP server that needs API Gateway capabilities and real streaming:

transport: http
streaming: true

And for cases where a Function URL is sufficient:

transport: url
streaming: true

The application doesn't need a new MCP abstraction for each one.

REST API without streaming is supported too

Streaming and transport are intentionally separate choices.

For example:

custom:
  pythonMcp:
    servers:
      orders:
        handler: app.handler
        transport: http
        streaming: false

uses API Gateway REST API but invokes the Python Lambda normally.

The application uses:

app = APIGatewayRestResolver()

This can be useful when REST API features are desired but incremental MCP streaming isn't.

The deployment model therefore isn't simply:

HTTP API = simple
REST API = streaming

It's more accurately:

                       Buffered       Streaming

HTTP API v2               ✓               -

REST API v1               ✓               ✓

Function URL              ✓               ✓

Paths and multiple MCP servers

Each server can expose its own path:

custom:
  pythonMcp:
    servers:
      orders:
        handler: orders.handler
        transport: http
        streaming: true
        path: /orders/mcp

      inventory:
        handler: inventory.handler
        transport: http
        streaming: true
        path: /inventory/mcp

The Python application registers the same path:

app.include_mcp(
    mcp,
    path="/orders/mcp",
)

Servers using API Gateway can share the underlying API while keeping separate Lambda functions and MCP endpoints.

That makes it possible to expose multiple domain capabilities without building one giant MCP server.

Authentication remains infrastructure

The plugin also deliberately doesn't invent an MCP-specific authentication model.

For HTTP API, existing Serverless authorizer configuration can be used.

For REST API, the plugin passes authorizer configuration through to the normal Serverless REST API event compiler.

Function URLs can use their supported public or AWS IAM modes.

So the architecture remains:

MCP Client
    ↓
AWS authentication / authorizer
    ↓
MCP transport
    ↓
middleware / application authorization
    ↓
tool

This keeps authentication independent from the MCP programming model.

Streaming should be earned

An important lesson from building this was that streaming shouldn't become the default architecture just because MCP supports it.

For many servers:

HTTP API v2
+
Lambda
+
modmex-lambda

is enough.

It's lightweight and fits the serverless execution model extremely well.

Streaming becomes useful when the operation actually has intermediate information worth delivering.

Then we can move to:

REST API
+
Lambda response streaming
+
Lambda Web Adapter
+
modmex-lambda

without redesigning tools, resources, prompts, middleware, or application services.

A note about cancellation

There is one serverless behavior worth understanding.

A client disconnect doesn't guarantee that the Lambda invocation immediately stops.

There are multiple network boundaries between the MCP client and the Python process:

MCP Client
    ↓
API Gateway
    ↓
Lambda
    ↓
Lambda Web Adapter
    ↓
Python

When the transport can observe a disconnect, modmex-lambda can propagate cooperative cancellation through MCPContext.

A long-running tool can therefore check:

if ctx.cancelled:
    return {"status": "cancelled"}

But applications shouldn't assume that every downstream network failure will immediately terminate a running Lambda invocation.

Response streaming and distributed execution cancellation are separate concerns.

One programming model, different infrastructure

The final architecture looks like this:

                         MCPServer
                            │
             ┌──────────────┴──────────────┐
             │                             │
             ▼                             ▼
      Buffered execution             Streamable execution
             │                             │
      regular Lambda                  HTTP process
             │                             │
             ▼                             ▼
   HTTP API / REST / URL            Lambda Web Adapter
                                           │
                                           ▼
                                    Lambda streaming

The important part is what doesn't change:

tools
resources
prompts
dependency injection
middleware
application services

Streaming is an infrastructure capability, not a new application architecture.

Open source

The complete implementation is available in two projects:

modmex-lambda contains the Python MCP runtime and application integration.

It provides the MCP server, tools, resources, prompts, middleware, dependency injection, protocol validation, buffered HTTP transports, and Streamable HTTP support.

serverless-python-mcp provides the Serverless Framework deployment integration.

It creates MCP Lambda functions from custom.pythonMcp.servers and configures the appropriate AWS front door and runtime behavior for buffered or streaming execution.

The design goal behind both projects is straightforward:

You don't need streaming to run MCP on Lambda. But when you need it, you shouldn't have to rewrite your MCP server.

Start with the smallest architecture that works.

Add streaming when the workload earns the complexity.

Keep the Python application the same.