Build a Daily Briefing Agent That Learns What You Care About

July 2026  |  6 min read  |  haao.ai Team

A generic morning briefing is basically useless. Tech headlines you already saw on Twitter. Market summaries covering sectors you do not invest in. Sports scores for teams you do not follow. The promise of AI briefing agents is that they should be smarter than this. They should know what matters to you and filter everything else out. Most of them do not because they have no memory of who you are.

The result is a new generation of AI morning emails that feel just as generic as the old ones. They use LLMs to summarize content, but the content selection is still driven by popularity algorithms, not personal relevance. The personalization layer is missing because it requires persistent memory of your preferences, and most briefing tools do not have that.

What a Truly Personalised Briefing Looks Like

A genuinely personalised briefing agent knows your topic hierarchy. At the top level, you care about AI regulation, your competitors in the SaaS space, and interest rate movements. Within AI regulation, you specifically care about the EU and the US, not Asia-Pacific. Within competitor news, you want product launches and funding rounds but not hiring announcements. These are not settings in a dropdown menu. They are nuanced preferences that can only be expressed in natural language and stored as structured memories.

A memory-enabled briefing agent builds this picture over time. The first time you say "skip the crypto section, I do not invest there," it stores that preference. The next time you say "that merger story on Acme Corp was the most useful thing in last week's briefing," it stores that signal too. Over weeks, the briefing gets tighter and more useful because the agent is accumulating a real model of your interests.

The Architecture of a Learning Briefing Agent

The architecture has two loops: a preference update loop and a daily generation loop. In the preference update loop, you interact with the briefing and give feedback. That feedback gets stored as memories tagged to your user profile. In the generation loop, the agent runs each morning, retrieves your current preference set, uses it to filter and prioritize the day's news, and generates a briefing shaped around your actual interests.

The key insight is that the preference retrieval step happens before the content selection step, not after. Most systems do it the other way around: they select a broad pool of content and then try to filter it with your preferences. A memory-first approach inverts this. Your preferences shape what gets pulled into consideration from the start, which produces a far more relevant output.

POST https://api.haao.ai/v1/memory/
Authorization: Bearer YOUR_API_KEY
{
  "content": "High priority topics: EU AI regulation, Series B SaaS funding rounds, Fed interest rate decisions. Low priority: crypto, sports, entertainment.",
  "tags": ["user:parker", "type:briefing-preferences", "version:2"],
  "agent_id": "briefing-agent-01"
}

POST https://api.haao.ai/v1/memory/
{
  "content": "User rated Acme Corp acquisition story 5/5. User skipped all three crypto items. Increase weight on M&A in SaaS, reduce crypto.",
  "tags": ["user:parker", "type:briefing-feedback", "date:2026-07-10"]
}

GET https://api.haao.ai/v1/memory/search?q=briefing+preferences+feedback&tags=user:parker&sort=recent&limit=8