We are building AI systems wrong.

Not slightly wrong. Fundamentally, structurally, catastrophically wrong.

The pattern is always the same. A team discovers the magic of a Large Language Model. They wrap it in a Python script. They give it access to the database, the API gateway, and the customer support logs. They dump three gigabytes of documentation into the context window because "1 million tokens" sounds like infinite storage.

They call it an "Agent."

In reality, they have built a God Agent. A monolithic, omniscient, undifferentiated blob of logic that tries to be the CEO, the janitor, and the database administrator simultaneously.

And it fails.

It hallucinates. It gets confused. It costs a fortune in token usage. The latency creeps up until the user experience feels like waiting for a dial-up connection in 1999. When it breaks (and it always breaks) the engineers cannot debug it because the logic isn't in the code. It’s in a probabilistic haze of prompt engineering and context pollution.

I have spent the last year tearing these systems apart. The solution isn't a better prompt. It isn't a bigger model. The solution is architecture.

Full technical analysis with code and benchmarks →

Why Are We Treating 1 Million Tokens Like Infinite RAM?

The current orthodoxy in AI development is seduced by the "Context Window Myth."

We have been sold a lie. The lie is that if you give a model enough context, it can solve any problem. Vendors push "infinite context" as the ultimate feature. 128k. 1 million. 2 million tokens. The implication is seductive. Don't worry about architecture. Don't worry about data curation. Just dump it all in. The model will figure it out.

This has led to the rise of the God Agent paradigm.

In this worldview, an "Agent" is a singular entity. It holds the entire state of the application. It has access to every tool in the library. When a user asks a question, the God Agent receives the query, looks at its massive context (which contains the entire history of the universe), and attempts to reason its way to an answer.

It feels like progress. It looks like the sci-fi dream of a singular, conscious AI.

But in production, this is a nightmare.

We are effectively asking a junior developer to memorize the entire codebase, the company handbook, and the legal archives, and then asking them to fix a CSS bug in 30 seconds.

They won't fix the bug. They'll have a panic attack.

Why Does My Agent Cost $50 to Say 'I Don't Know'?

The cracks in the God Agent architecture are visible to anyone pushing code to production. It usually manifests in four ways.

1. Context Pollution (The Needle in the Haystack) The more information you provide, the less attention the model pays to the critical bits. This is not just a feeling. It is an architectural flaw. Research shows that models struggle to retrieve information from the middle of long contexts. By failing to curate, we actively harm performance. We create systems where the "noise" of irrelevant documentation overpowers the "signal" of the user's specific intent.

2. Latency and Cost Every token costs money. Every token takes time to process. A God Agent that re-reads a 50k token context for every turn of conversation is burning cash. It is computationally wasteful. We are running a supercomputer to answer "yes" or "no" because we didn't bother to filter the inputs.

3. The Debugging Black Hole When a God Agent fails, why did it fail? Was it the prompt? The retrieval step? The tool output? Or did it just get distracted by an irrelevant piece of text from page 405 of the documentation? You cannot unit test a prompt that changes its behaviour based on the variable soup of a massive context window.

4. The Governance Void A single agent with access to everything is a security nightmare. If the prompt injection works, the attacker owns the castle. There are no bulkheads. There is no "zero trust" because the architecture relies on maximum trust in a probabilistic model.

Is The Solution Just Microservices (Again)?

Yes. It is.

The path forward is Aggressive Context Curation and the Agentic Mesh.

We must shatter the God Agent. We must replace it with a network of small, specialized, highly constrained agents that communicate via standardized protocols.

In a mesh architecture, no single agent knows everything.

They do not share a context window. They share messages.

This is the shift from a monolith to microservices. It is the only way to scale complexity. When the Support Agent is working, it doesn't need to know the database schema. It doesn't need the Python libraries. Its context is pristine. It is curated.

Let's look at the difference in code structure.

The Old Way: The God Prompt

This is what most people are writing today. It's a mess.

# GOD AGENT - ANTI-PATTERN
# We dump everything into one system prompt.

system_prompt = """
You are an omniscient AI assistant for Acme Corp.
You have access to:
1. The User Database (Schema: users, orders, items...)
2. The Codebase (Python, React, TypeScript...)
3. The Company Handbook (HR policies, returns, holidays...)
4. The Marketing Style Guide

Instructions:
- If the user asks about SQL, write a query.
- If the user asks for a refund, check the handbook policy then query the DB.
- If the user asks for code, write Python.

Current Context:
{entire_rag_retrieval_dump}
{last_50_messages}
"""

# Result: The model gets confused.
# It tries to apply HR policies to SQL queries.
# It hallucinates tables that don't exist.

python

The New Way: The Agentic Mesh

Here, we split the logic. The Router doesn't do the work. It delegates.

# MESH ARCHITECTURE - PATTERN
# Step 1: The Router Agent
# Its only job is to classify and route. It has NO domain knowledge.

router_prompt = """
You are a routing system.
Analyze the user input and route to the correct agent.

Available Agents:
1. billing_agent (Refunds, invoices, payments)
2. tech_support_agent (Python, SQL, Bug fixes)
3. general_chat_agent (Casual conversation)

Output JSON only: {"target_agent": "name", "reasoning": "string"}
"""

# Step 2: The Specialist Agent (Billing)
# This agent loads ONLY when called.
# It has zero knowledge of Python or SQL.

billing_agent_prompt = """
You are a Billing Specialist.
You handle refunds and invoices.

Tools available: [stripe_api, invoice_db]

Context:
{user_transaction_history_only}
{refund_policy_summary}
"""

python

See the difference? The billing_agent cannot hallucinate SQL syntax because it doesn't know what SQL is. Its universe is small. Small universes are hallucination-resistant.

How Do Agents Actually Talk Without Hallucinating?

I have been skeptical of big tech frameworks. They usually add bloat. I like raw code.

But Google's Agent Development Kit (ADK) and the Agent-to-Agent (A2A) protocol are different. They are trying to solve the plumbing problem.

Google has realised that if we want agents to work, they need to talk to each other like software, not like chatbots.

The A2A Protocol

This is the game changer. The A2A protocol is a vendor-neutral standard for agents to discover and talk to each other. It uses "Agent Cards". These are standardized JSON metadata files that describe what an agent can do.

Think of it like this:

{
  "agent_id": "billing_specialist_v1",
  "capabilities": ["process_refund", "check_invoice_status"],
  "input_schema": {
    "type": "object",
    "properties": {
      "transaction_id": {"type": "string"},
      "user_intent": {"type": "string"}
    }
  },
  "output_schema": {
    "type": "object",
    "properties": {
        "status": {"type": "string", "enum": ["success", "failed"]},
        "refund_amount": {"type": "number"}
    }
  }
}

json

When a Router Agent needs to process a refund, it doesn't try to hallucinate the API call. It looks up the billing_specialist, handshakes via A2A, passes the structured payload, and waits for a structured response.

This is standardisation. It allows us to build an Agentic Mesh where agents from different teams, or even different companies, can collaborate.

This solves the "isolated islands" problem. Currently, an OpenAI agent cannot talk to a Vertex AI agent. With A2A, they share a protocol. They negotiate.

What This Actually Means

Adopting a mesh architecture changes everything about how we build.

1. Observability is Mandatory You cannot grep the logs of a probabilistic mesh. Traditional observability (logs, metrics, traces) is insufficient. We need Agentic Observability. We need to see the reasoning chain. Why did the Router hand off to the Billing Agent? Why did the Billing Agent reject the request? We need to trace the cost and latency per node in the mesh. If you don't have this, you aren't building a system. You're building a casino.

2. Zero Trust Security In a God Agent model, security is a binary switch. In a mesh, we can apply Zero Trust. The Billing Agent does not trust the Router Agent implicitly. It verifies the payload. It checks the policy. It limits the blast radius.

3. The End of "Prompt Engineering" Prompt engineering as a standalone discipline is dying. It is being replaced by System Engineering. The prompt is just a function configuration. The real work is in the routing logic, the schema definition, and the context curation strategy.

4. Aggressive Context Curation We must become ruthless editors. The goal is not to fill the context window. The goal is to empty it. We need to compress. We need to summarize. We need to inject only exactly what is needed for the next immediate step. If an agent is tasked with writing SQL, it needs the schema. It does not need the company mission statement.

(Sounds obvious. Yet I see it ignored in 90% of codebases.)

Read the complete technical breakdown →

TL;DR For The Scrollers