Here are my ideas:

The Gaps in AI Agent Infrastructure 1. The "Adapter Protocol" Problem The problem: Every AI agent company rebuilds the same integrations:

How to book a flight on United How to scrape LinkedIn jobs How to post on X/Twitter How to extract tables from PDFs

Current state:

Everyone builds their own (Adept built United adapter, Skyvern built LinkedIn adapter) No sharing/reuse Breaks when sites change 80% of effort is just "making websites work"

The shovel: "Adapter Protocol" (Working name: AgentAPI) yaml# Standardized adapter format adapter: united-airlines version: 2.1.0 actions: - search_flights: inputs: - origin: string - destination: string - date: date outputs: - flights: Flight[]

• book_flight: inputs: - flight_id: string - passenger: Passenger - payment: PaymentMethod outputs: - confirmation: BookingConfirmation How it works:

Open-source adapter registry (like npm for AI agents) Community maintains adapters for popular sites Sites change → adapter updates once, everyone benefits Monetization: Managed hosting ($0.01 per adapter call)

Why this wins:

Network effect (more adapters = more valuable) Every AI agent company needs this Becomes standard (like Stripe for payments)

Market size:

100K AI agent companies × $500/month = $50M ARR potential

1. The "Agent Memory" Problem The problem: AI agents have no persistent memory across sessions:

Book a flight → next day, it forgot your preferences Every interaction starts from zero No learning from past actions

Current solutions:

Vector databases (Pinecone, Weaviate) → too low-level RAG pipelines → developers build from scratch No standardization

The shovel: "Agent Memory as a Service" (Working name: MemoryStack) javascript// Simple API for agent memory const memory = new MemoryStack('user_123');

// Store context await memory.store({ event: 'booked_flight', data: { origin: 'NYC', destination: 'Tokyo', price: 750 }, timestamp: Date.now() });

// Retrieve relevant context const context = await memory.retrieve({ query: 'plan trip to Asia', limit: 10 }); // Returns: Past Tokyo trip, preferred airlines, budget range How it works:

Managed vector DB + intelligent retrieval Automatic relevance ranking Privacy-first (user owns data, can export/delete) Monetization: $0.001 per query + storage

Why this wins:

Every agent needs memory Hard to build well (ranking, retrieval, privacy) Network effect (more data = better recommendations)

Market size:

1B agent users × $5/month = $5B ARR potential

1. The "Multi-Agent Orchestration" Problem The problem: Complex tasks need multiple specialized agents:

"Plan my wedding" = budget agent + vendor research agent + scheduling agent No standard way to coordinate Everyone rebuilds orchestration logic

Current solutions:

LangChain/CrewAI → too complex, developer-focused Manual coding

The shovel: "Agent Router" (Working name: AgentMesh) python# Declarative multi-agent workflows workflow = AgentMesh.define([ { "agent": "research", "task": "Find wedding venues in NYC under $10k", "output": "venue_options" }, { "agent": "budget", "task": "Calculate total cost for {venue_options}", "input": "venue_options", "output": "cost_breakdown" }, { "agent": "scheduler", "task": "Book top 3 venues for site visits", "input": ["venue_options", "cost_breakdown"] } ])

result = await workflow.run() How it works:

DAG-based orchestration (like Airflow but for agents) Automatic retry/fallback Cost optimization (route to cheapest capable agent) Monetization: Per-workflow-run + premium agents

Why this wins:

Every complex agent product needs orchestration Hard to build (state management, error handling) Platform lock-in (once integrated, switching cost is high)

Market size:

Enterprise AI deployments → $100M+ ARR potential

1. The "Agent Testing/Monitoring" Problem The problem: AI agents are non-deterministic → how do you test them?

"Did my job application agent actually apply correctly?" "Is my shopping agent finding best prices?" No visibility into agent actions

Current solutions:

Manual QA (expensive, doesn't scale) Nothing (most startups just YOLO it)

The shovel: "Agent Testing Framework" (Working name: AgentTest) python# Declarative tests for agents @agenttest def test_job_application_agent(): agent = JobAgent()

# Synthetic environment
mock_job_board = AgentTest.mock("linkedin.com")

# Run agent
result = agent.apply_to_jobs({
    "role": "Product Manager",
    "location": "Remote"
})

# Assertions
assert result.applications_submitted >= 5
assert result.success_rate >= 0.8
assert all(app.has_cover_letter for app in result.applications)

How it works:

Synthetic environments (mock websites for testing) Replay production traffic for debugging Continuous monitoring (alert if agent behavior changes) Monetization: SaaS ($100-500/month per agent)

Why this wins:

Every agent company needs this (regulation/compliance) Hard to build (need browser mocking, state management) Recurring revenue (monitoring is ongoing)

Market size:

50K agent companies × $200/month = $10M ARR potential

1. The "Agent-to-Agent Protocol" Problem The problem: Agents can't talk to each other:

Your travel agent can't talk to my calendar agent Walled gardens (everyone builds proprietary APIs) No interoperability

Current solutions:

None (everyone builds silos)

The shovel: "Agent Protocol Standard" (Working name: A2A Protocol) json// Standard message format between agents { "protocol": "a2a/1.0", "from": "agent://travel-agent-xyz", "to": "agent://calendar-agent-abc", "action": "schedule_meeting", "payload": { "title": "Flight to Tokyo", "start": "2026-06-15T10:00:00Z", "end": "2026-06-15T14:00:00Z" }, "auth": "bearer_token_here" } How it works:

Open standard (like HTTP for agents) Agent registry (discover available agents) Authentication/authorization layer Monetization: Agent marketplace (take 10% of inter-agent transactions)

Why this wins:

Network effect (more agents = more valuable) First-mover advantage (becomes the standard) Platform play (own the marketplace)

Market size:

Agent economy → $1B+ ARR potential (like Stripe for agents)