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daniele pelleri
daniele pelleri

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5 Brutal Lessons from Building a Multi-Agent AI System (And How to Avoid My Epic Fails)

#ai #architecture #multiagent #watercooler

What happens when you go from "hello world" AI to orchestrating an entire team of agents that need to collaborate without destroying each other? Spoiler: everything that can go wrong, will go wrong.

After 6 months of development and $3,000 burned in API calls, I learned some brutal lessons building an AI orchestration system. This isn't your typical polished tutorial—these are the real epic fails nobody tells you about in those shiny conference presentations.

🔥 Lesson #1: "The Agent That Wanted to Hire Everyone"

The Fail: My Director AI, tasked with composing teams for projects, consistently created teams of 8+ people to write a single email. Estimated budget: $25,000 for 5 lines of text.

The Problem: LLMs, when unconstrained, tend to "over-optimize." Without explicit limits, my agent interpreted "maximum quality" as "massive team."

The Fix:

# Before (disaster) prompt = "Create the perfect team for this project" # After (reality) prompt = f""" Create a team for this project. NON-NEGOTIABLE CONSTRAINTS: - Max budget: {budget} USD - Team size: 3-5 people MAX - If you exceed budget, proposal will be automatically rejected """
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Takeaway: AI agents without explicit constraints are like teenagers with unlimited credit cards.

⚡ Lesson #2: Race Conditions Are Hell

The Fail: Two agents grabbed the same task simultaneously, duplicating work and crashing the database.

WARNING: Agent A started task '123', but Agent B had already started it 50ms earlier. ERROR: Duplicate entry for key 'PRIMARY' on table 'goal_progress_logs'.
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The Problem: "Implicit" coordination through shared database state isn't enough. In distributed systems, 50ms latency = total chaos.

The Fix: Application-level pessimistic locking

# Atomic task acquisition update_result = supabase.table("tasks") \ .update({"status": "in_progress", "agent_id": self.id}) \ .eq("id", task_id) \ .eq("status", "pending") \ .execute() if len(update_result.data) == 1: # Won the race - proceed  execute_task(task_id) else: # Another agent was faster - find another task  logger.info(f"Task {task_id} taken by another agent")
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Takeaway: In multi-agent systems, "probably works" = "definitely breaks."

💸 Lesson #3: $40 Burned in 20 Minutes of CI Tests

The Fail: My integration tests made real calls to GPT-4. Every GitHub push = $40 in API calls. Daily budget burned before breakfast.

The Problem: Testing AI systems without mocks is like load-testing with a live credit card.

The Fix: AI Abstraction Layer with intelligent mocks

class MockAIProvider: def generate_response(self, prompt: str) -> str: # Deterministic responses for testing  if "priority" in prompt.lower(): return '{"priority_score": 750}' return "Mock response for testing" # Environment-based switching if os.getenv("TESTING"): ai_provider = MockAIProvider() else: ai_provider = OpenAIProvider()
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Result: Test costs down 95%, speed up 10x.

Takeaway: An AI system that can't be tested cheaply is a system that can't be developed.

🌀 Lesson #4: The Infinite Loop That Never Ends

The Fail: An "intelligent" agent started creating sub-tasks of sub-tasks of sub-tasks. After 20 minutes: 5,000+ pending tasks, system completely frozen.

INFO: Agent A created Task B INFO: Agent B created Task C INFO: Agent C created Task D ... [continues for 5,000 lines] ERROR: Workspace has 5,000+ pending tasks. Halting operations.
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The Problem: Autonomy without limits = autopoietic chaos.

The Fix: Anti-loop safeguards

# Task delegation depth limit if task.delegation_depth >= MAX_DEPTH: raise DelegationDepthExceeded() # Workspace task rate limiting if workspace.tasks_created_last_hour > RATE_LIMIT: workspace.pause_for_cooldown()
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Takeaway: Autonomous agents need "circuit breakers" more than any other system.

🎭 Lesson #5: AI Has Its Own Bias (Not the Ones You Think)

The Fail: My AI-driven prioritization system systematically preferred tasks that "sounded more important" vs tasks that were actually business-critical.

The Problem: LLMs optimize for "sounding right" not "being right." Bias toward pompous corporate language.

The Fix: Objective metrics + AI reasoning

def calculate_priority(task, context): # Objective factors (non-negotiable)  base_score = ( task.blocked_dependencies_count * 100 + task.age_days * 10 + task.business_impact_score ) # AI enhancement (subjective)  ai_modifier = get_ai_priority_assessment(task, context) return min(base_score + ai_modifier, 1000) # Cap at 1000
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Takeaway: AI for creativity, deterministic rules for critical decisions.

🚀 What's Next?

These are just 5 of the 42+ lessons I documented building this system. Each fail led to architectural patterns I now use systematically.

The journey from "single agent demo" to "production orchestration system" taught me that the real engineering isn't in the AI—it's in everything around it: coordination, memory, error handling, cost management, and quality gates.

Question for the community: What's been your most epic fail working with AI/agents? How did you solve it?

If anyone's facing similar challenges in AI orchestration, happy to dive deeper into the technical details. This rabbit hole goes deep!

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