The Demo vs Reality Gap
Every week there’s a new “autonomous AI agent” demo on Twitter. An agent that browses the web, writes code, deploys to production. Impressive demos. Terrible production systems.
The gap comes down to three things: reliability, cost, and observability.
Architecture That Survives Production
Our platform uses a hierarchical task decomposition model:
- Orchestrator — Breaks high-level goals into subtasks
- Specialists — Domain-specific agents that handle subtasks
- Validator — Checks outputs before they propagate
The key insight: agents should be narrow and reliable, not general and impressive.
class AgentOrchestrator:
def decompose(self, task: Task) -> list[SubTask]:
"""Break task into independently executable subtasks."""
plan = self.planner.generate_plan(task)
return self.validator.check_plan(plan)
async def execute(self, subtasks: list[SubTask]):
"""Execute subtasks with dependency resolution."""
graph = build_dependency_graph(subtasks)
for batch in topological_batches(graph):
results = await asyncio.gather(
*[self.dispatch(st) for st in batch]
)
self.memory.store_results(results)Memory Is Everything
Agents without memory are just expensive API calls. Our memory system has three layers:
- Working memory — Current task context, recent outputs
- Episodic memory — Past task executions, what worked and what didn’t
- Semantic memory — Domain knowledge, indexed for retrieval
The Cost Problem
A single complex task can trigger hundreds of LLM calls. Without guardrails, costs explode. We implement:
- Token budgets per task
- Caching at every layer
- Fallback to smaller models for simple subtasks
- Circuit breakers that halt runaway agents
What I’d Do Differently
Start with deterministic workflows and add AI at the edges. Not the other way around. The most reliable agent systems are 80% traditional software and 20% LLM magic.