Building a Reliable Network for AI Prediction Agents

Teams running AI prediction agents on platforms like Polymarket and Kalshi spend weeks tuning models and backtesting strategies, then watch accounts get flagged within days because the agent logged in from three different data-center IPs in two countries. Platform trust systems don't care how sophisticated the forecasting algorithm is if the network footprint screams automation.

The issue isn't the prediction logic. It's that most AI prediction agents operate through infrastructure built for speed and cost, not consistency. Cheap residential proxies rotate through hundreds of exit nodes. Platforms designed to prevent manipulation have gotten very good at spotting these patterns.

What separates agents that run for months from those that get suspended in days? The identity and connectivity layer nobody talks about.

Why Standard Infrastructure Fails on Polymarket and Kalshi

Prediction markets enforce strict anti-abuse controls because real money moves on forecasts. Prediction market agents need to look like consistent users, not rotating proxies hitting APIs at scale.

Data-Center and Residential IP Problems

Data-center IPs are the first problem. Most cloud providers and VPS hosts operate from ASN ranges that platforms flag immediately. Running an agent from AWS or DigitalOcean works until the first account review, when the platform sees dozens of other flagged accounts came from the same subnet recently. Data-center proxies trace back to server farms, not residential ISPs, making them trivial to identify.

Residential proxy pools sound better but introduce different issues. These services route traffic through consumer devices and cycle IPs constantly. An agent might appear to log in from Texas at 9am, then Florida at 9:15am, then back to Texas by 10am. Prediction platforms track geolocation consistency as a core trust signal.

Phone Verification Where Required

Some prediction markets require phone verification as part of their KYC process. Kalshi requires phone number verification for all accounts, with SMS codes used for both signup and 2FA. Polymarket's standard accounts use wallet-based authentication, but their US market access requires phone verification for waitlist and identity confirmation.

When phone verification is needed, VoIP numbers from services like Google Voice get flagged by carrier lookup APIs that platforms use to detect non-carrier numbers. Real mobile numbers from carrier infrastructure pass these checks because they're indistinguishable from consumer phones.

Session Fingerprinting Goes Beyond IP

Session fingerprinting has gotten sophisticated. Platforms don't just check IP addresses and phone numbers anymore. They analyze TCP fingerprints, TLS handshake patterns, HTTP header orders, and timing behaviors. An agent connecting through a proxy service that strips or normalizes these signals stands out precisely because it looks too clean.

The infrastructure that works for web scraping or social media automation doesn't translate to prediction market agents that need to maintain long-term platform trust.

Building Identity Infrastructure That Lasts

Let's look at what actually works for long-term operation.

Reliable AI prediction agents start with stable network identities that mirror real user behavior. The foundation is mobile IPs tied to specific geographic regions - and for platforms requiring phone verification, carrier-based numbers that pass lookup checks.

Mobile Proxies: The Core of Agent Infrastructure

Mobile proxies built on cellular infrastructure solve the IP consistency problem. Instead of rotating through random residential endpoints, these proxies route traffic through actual 4G/5G connections with stable geographic presence. An agent can maintain the same mobile IP for extended periods, appearing to platforms as a consistent user in a specific metro area.

Regional routing becomes critical here. If an agent targets US prediction markets, it needs a US mobile IP that stays within a realistic geographic radius. Platforms don't expect users to sit in the exact same location 24/7, but they do expect movement patterns that make sense. Not jumps between continents or sudden switches between mobile and data-center networks.

Why do mobile IPs work so well? CGNAT (Carrier-Grade NAT) means hundreds of legitimate users share the same mobile IP address at any time. Platforms can't block mobile IP ranges without affecting massive numbers of real customers.

Phone Verification for KYC-Required Platforms

For platforms like Kalshi that require phone verification, the same principle applies: carrier-based numbers pass checks that VoIP fails. Services that provide SMS verification through real carrier SIMs give agents the same trust profile as organic users. The number has proper routing information, carrier history, and isn't flagged in VoIP databases.

Technical Setup for Long-Term Operation

Account Aging and Initial Setup

Start by establishing a base identity before any agent activity. Register accounts manually through mobile IPs, complete any required verification steps, and let the account age for 48-72 hours before automation begins. Platforms track account age as a trust factor. Brand-new accounts making API calls immediately look suspicious.

Configure the agent to maintain sticky sessions. Mobile proxies typically offer session persistence where the same IP stays active for a defined period. For prediction market agents, longer sessions reduce the frequency of IP changes and create more natural activity patterns. Our platform offers 60-minute sticky IP sessions as a baseline.

But don't keep the same IP forever. Real mobile users move between cell towers, switch from mobile to WiFi, and occasionally restart their devices. A prediction market agent that connects from the identical IP for weeks straight looks automated. Rotate IPs gradually, within the same geographic region and carrier network.

Rate Limiting and Human-Like Timing

Rate limiting matters more than speed. Prediction platforms expect human-paced interactions, not millisecond-perfect API calls. Add random delays between requests, vary the timing distribution, and avoid perfect intervals. Bot detection systems analyze request timing patterns, so adding jitter significantly reduces flag rates compared to fixed intervals.

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import time, random
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from datetime import datetime, timedelta
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class PredictionAgent:
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  def __init__(self, proxy):
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      self.proxy = proxy
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      self.session_start = datetime.now()
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      self.session_length = timedelta(minutes=random.randint(45, 75))
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  def maybe_rotate(self):
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      if datetime.now() - self.session_start > self.session_length:
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          self.proxy.rotate(same_region=True)
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          self.session_start = datetime.now()
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          time.sleep(random.uniform(30, 60))
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  def human_delay(self):
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      delay = random.uniform(3, 8)
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      if random.random() < 0.15:
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          delay += random.uniform(10, 30)
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      time.sleep(delay)
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  def place_forecast(self, market_id, position, amount):
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      self.maybe_rotate()
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      self.human_delay()
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      return self.proxy.post(f"https://api.example.com/markets/{market_id}/predict",
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                             json={"position": position, "amount": amount})

Keeping Agents Running

AI prediction agents need infrastructure that matches their operational timeline. Short-term bots can get away with shortcuts. Agents meant to run for months need identity and network layers that platforms trust.

That means mobile IPs with geographic consistency, session patterns that mirror real users, and carrier-based verification where platforms require it. The connectivity layer isn't glamorous, but it's what separates agents that survive from those that get flagged before they prove their models work.

For teams building sophisticated prediction systems, the infrastructure strategy matters as much as the forecasting model. We've covered similar territory in our guide on coordinating AI agent networks and the infrastructure gap in the AI agent market.

FAQ

Note: Automated trading may be subject to platform terms of service. Review each platform's policies before deploying automated systems.