Building Synter with Amp by Sourcegraph

Why 'AI Media Agent'?

Traditional paid media management is broken. Agencies charge 10-15% fees, campaigns take weeks to launch, and optimization happens manually (if at all). We saw an opportunity to build an AI media agent that could:

Plan campaigns from a brief using frontier LLMs (GPT-4o, Claude 3.5 Sonnet)
Launch across 6 platforms (Google, Microsoft/Bing, LinkedIn, Meta, Reddit, X) simultaneously
Optimize autonomously with explainable changes and rollback

The result: autonomous media buying that's faster, cheaper, and more data-driven than human-only management.

Enter Amp: Agentic Coding Framework

We chose Amp by Sourcegraph as our development framework because it's purpose-built for agentic systems. Amp provides:

Task routing: Send briefs to planning agents, send optimization requests to analysis agents
Tool orchestration: Agents can call Google Ads API, LinkedIn Campaign Manager, Meta Marketing API, etc.
Audit trail: Every agent action is logged with rationale and metrics
BYOK model support: Swap OpenAI for Anthropic or Google without rewriting code

Instead of building task queues, API wrappers, and logging systems from scratch, we leveraged Amp's primitives and focused on domain logic.

Architecture: Multi-Agent System

Synter uses a multi-agent architecture where each agent has a specialized role:

1. Research Agent (Long-Context Models)

Model: Claude 3.5 Sonnet (200k token context)

Responsibilities: Analyze website content, competitor research, industry reports. Extract ICP signals, pain points, and messaging angles.

2. Planning Agent (Reasoning Models)

Model: GPT-4o or o-series

Responsibilities: Take campaign brief + research → generate channel mix, budget allocation, audience targeting, keyword lists, ad copy variants.

3. Creative Agent (Vision Models)

Model: Gemini 1.5 Pro (multimodal)

Responsibilities: Generate ad copy (headlines, body, CTAs), analyze existing creative for patterns, suggest image/video concepts.

4. Optimization Agent (Fast Reasoning Models)

Model: GPT-4o or Llama 3.3 (cost-effective)

Responsibilities: Monitor performance daily, recommend budget shifts, creative rotations, negative keywords, audience adjustments.

Platform Integrations: 6 Ad Platforms, 1 Unified Interface

Building native integrations with Google Ads, Microsoft Ads, LinkedIn, Meta, Reddit, and X required wiring OAuth flows, API clients, and entity normalization.

OAuth & Scopes

Each platform requires OAuth 2.0 with specific scopes. We built a unified OAuth handler in Amp that:

Redirects users to platform authorization pages
Stores encrypted tokens (access + refresh) in the database
Auto-refreshes tokens before expiry

Agent Tools for Platform Actions

Amp's tool system lets agents call platform APIs as functions. Example tools:

create_google_ads_campaign() → Creates campaign + ad groups + ads
update_linkedin_budget() → Adjusts daily budget
add_negative_keywords() → Adds negatives at campaign/ad group level

Results: Faster, Cheaper, Smarter

With Amp powering Synter's agentic architecture, we've achieved:

2-3x

Faster campaign launch (minutes vs. days)

35-50%

Lower CAC with AI optimization

$50k-$200k

Annual savings (no agency fees)

What's Next: Agentic Ad Buying at Scale

We're continuing to expand Synter's capabilities with Amp:

TikTok and Snapchat integrations (Q1 2026)
Multi-touch attribution models (time-decay, position-based)
Advanced creative testing (image generation, video analysis)
Custom model fine-tuning on customer-specific conversion data

If you're building agentic systems or exploring autonomous media buying, we'd love to hear from you.