# Building Synter with Amp by Sourcegraph | Synter Blog

> Source: https://syntermedia.ai/blog/building-synter-with-amp
> Cached: 2026-05-14T08:52:19.659Z
> Words: 576

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[Back to Blog](https://syntermedia.ai/blog)## 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 10 platforms (Google, Meta, LinkedIn, Microsoft, TikTok, Reddit, X, Pinterest, Snapchat, DV360) 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](https://ampcode.com) 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 used 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:

2-3xFaster campaign launch (minutes vs. days)

35-50%Lower CAC with AI optimization

$50k-$200kAnnual 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.

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