Stop Building God Agents

Prelude

Contemporary AI systems are being constructed with fundamental architectural flaws. The typical pattern involves wrapping a language model in scripts, granting it access to databases and APIs, and flooding its context with documentation—then declaring it an "Agent."

What's actually been built is a monolithic system attempting multiple conflicting responsibilities simultaneously. These so-called God Agents hallucinate, become confused, consume excessive tokens, suffer from degraded latency, and create debugging nightmares because their logic resides in probabilistic patterns rather than structured code.

The author describes spending a year deconstructing these systems and arriving at a single conclusion: "The solution is architecture." Rather than seeking better prompting or larger models, teams must reorganize their approach around structured system design. The future belongs to distributed mesh architectures, not singular omniscient entities.

The Orthodoxy

The industry operates under a misleading assumption: providing sufficient context to a model enables it to solve any problem. Vendors market increasingly larger context windows—128k tokens, 1 million, 2 million—implying that architecture and curation become unnecessary.

This belief has spawned the God Agent paradigm: a singular entity holding complete application state with access to every available tool. When users submit queries, the God Agent attempts to reason through solutions using its massive context.

However, this approach conflates three distinct functions:

  1. Orchestration (planning and routing)
  2. Capabilities (specific skills)
  3. Execution (external tools and APIs)

By merging these layers, the system loses separation of concerns. The model must distinguish signal from noise within increasingly massive datasets—a task fundamentally degraded by context pollution. The effect resembles requiring someone to identify a specific bug while simultaneously memorizing entire codebases and legal archives.

The Cracks

Production deployments reveal systemic problems with God Agent architectures:

Context Pollution and Information Retrieval: Models demonstrate reduced performance retrieving information from lengthy contexts. Excessive, uncurated data overwhelms relevant details, forcing the system to process noise alongside signal.

Latency and Expense: Every token carries processing costs and computational overhead. A system re-reading 50,000 tokens per conversation turn wastes resources answering straightforward questions.

Debugging Opacity: When God Agents fail, determining the cause becomes impossible. Did the prompt fail? The retrieval mechanism? Tool outputs? Or did irrelevant documentation distract the model? Traditional testing cannot account for probabilistic behavior changes based on variable context.

Security Vulnerabilities: Centralized agents with universal access create catastrophic failure modes. Successful prompt injection compromises the entire system without compartmentalization.

The industry is recognizing this model as fundamentally flawed.

The Deeper Truth

The solution involves two principles: Aggressive Context Curation and Agentic Mesh architecture.

Instead of monolithic God Agents, systems should employ networks of specialized, constrained agents communicating through standardized protocols.

The Agentic Mesh Architecture

Each agent maintains focused expertise:

  • The Router Agent classifies intent
  • The Support Agent understands return policies
  • The Coding Agent knows Python syntax
  • The SQL Agent understands database schemas

Agents don't share context windows—they exchange structured messages. This parallels the evolution from monolithic applications to microservices. Specialization produces cleaner contexts and superior performance.

This architecture requires rigorous terminology:

  1. Agent: The orchestrator planning and reasoning toward goals
  2. Skill: The "how"—specific workflows or capabilities (e.g., drafting emails)
  3. Tool: The "what"—external resources and APIs

Separation enables optimization at each layer: skills become refinable, tools become sandboxable, agents become observable.

Google's ADK and A2A Framework

Google's Agent Development Kit and Agent-to-Agent protocol represent meaningful advancements in solving infrastructure challenges.

The ADK implements structured context management using tiered systems:

  • Session: Immediate conversational context
  • State: Dynamic variables (authentication status, user preferences)
  • Memory: Long-term storage and history
  • Artifacts: Discrete content units existing outside conversational flow, referenced by ID rather than included in context

This architecture maintains clean context windows—agents reference artifact identifiers rather than re-processing entire files.

The A2A protocol enables agent-to-agent communication through standardized vendor-neutral specifications using "Agent Cards"—JSON metadata describing capabilities, input requirements, and output formats.

When a Router Agent requires refund processing, it locates the billing specialist agent via A2A, negotiates handshake protocols, transmits structured payloads, and awaits formatted responses—eliminating hallucinated API calls and speculation.

This standardization allows cross-organizational collaboration: agents from competing providers can cooperate through unified protocols.

Implications

Mesh architecture adoption transforms development practices fundamentally:

1. Observability Becomes Mandatory

Traditional logging and metrics prove insufficient for probabilistic systems. Agentic Observability requires tracing reasoning chains: Why did routing select the Billing Agent? Why was the response rejected? Per-node cost and latency tracking becomes essential.

Without visibility, systems become unpredictable gambling operations.

2. Zero Trust Security

God Agents operate as binary trust switches. Mesh architectures enable Zero Trust principles: agents verify payloads, validate policies, and limit blast radius. The Billing Agent doesn't trust Router Agent assertions implicitly.

3. Prompt Engineering's Evolution

Standalone "prompt engineering" discipline is transitioning into System Engineering. Prompts become function configurations; real value emerges from routing logic, schema definitions, and context curation strategies.

4. Ruthless Context Curation

The objective shifts from filling context windows to emptying them. Teams must implement compression, summarization, and injection strategies ensuring agents receive only immediately necessary information. A SQL-writing agent needs database schema—not company mission statements.

Conclusion

The generative AI honeymoon phase has concluded. The industry is graduating from experimental demonstrations toward production systems requiring rigorous architectural thinking. Building God Agents represents technical immaturity; constructing mesh architectures demonstrates engineering discipline.