The Decision Dashboard: One Morning View for All Your Agents

Last issue, you built a persistent agent — a system that runs every morning, checks your world, and logs what it finds.

If you followed the advice, you might have two or three of them running now. A research monitor. A financial tracker. Maybe an email triage agent.

Here is the problem nobody warns you about: you now have three output files to check every morning instead of zero. You traded one kind of friction for another.

The fix is not fewer agents. It is a better front end.

This issue shows you how to build a decision dashboard — one page that pulls from all your agents, ranks what matters, and gives you a five-minute morning briefing. No digging through folders. No comparing timestamps. One view, prioritized.

The Architecture

A decision dashboard has three layers. You can build all three in 30 minutes.

Layer 1: The Collector

A prompt that reads output from all your agents and combines them into one data structure. Your agents already write to files. The collector just reads them and merges.

You are a dashboard collector. Every morning after all agents have run:

1. Read these output files:
   - ~/agent/output/research_brief_[TODAY].md
   - ~/agent/output/financial_scan_[TODAY].md
   - ~/agent/output/email_triage_[TODAY].md
   - ~/agent/state/state.json (for metrics and pending questions)

2. For each file, extract:
   - Items marked URGENT or flagged for attention
   - New findings (not present yesterday)
   - Pending questions the agent left for you
   - Key metrics (counts, changes, streaks)

3. Save the combined result to ~/dashboard/daily_brief_[TODAY].json

Layer 2: The Prioritizer

Raw data is not a dashboard. You need a second pass that ranks items by urgency and importance. This is where the real value lives.

You are a dashboard prioritizer. Read ~/dashboard/daily_brief_[TODAY].json
and create a prioritized morning view.

Priority rules:
- P0 (RED): Anything marked URGENT. Deadlines within 48 hours. Errors.
- P1 (YELLOW): New findings needing a decision. Agent questions.
  Metric changes >20%.
- P2 (GREEN): Informational. Routine updates. Normal metrics.

Output format (save to ~/dashboard/morning_view_[TODAY].md):

## [DATE] Morning Dashboard

### Needs Attention Now (P0)
[List items with source agent name]

### Decisions Needed (P1)
[List items with recommended action]

### For Your Awareness (P2)
[Brief summary, not individual items]

### Agent Health
- Research: [status] | Last run: [time] | Streak: [N] days
- Financial: [status] | Findings today: [N]
- Email: [status] | Emails processed: [N]

### Questions From Your Agents
[All pending questions with agent name]

Here is what your morning view looks like in practice:

Layer 3: The Renderer (Optional)

Turn the prioritized markdown into something visual. This is polish — skip it on day one. The markdown file is your dashboard until you need more.

You are a dashboard renderer. Read ~/dashboard/morning_view_[TODAY].md
and generate ~/dashboard/index.html.

Design rules:
- P0 items: red left border, appear first
- P1 items: yellow left border
- P2 items: collapsed by default (click to expand)
- Agent health: green/yellow/red status dots
- Top line: "Estimated review time: X minutes"
- Minimal design. No frameworks. One HTML file with inline CSS.
- Auto-refresh every 30 minutes.

The Step-by-Step Build

Step 1: Inventory Your Agents (5 minutes). List every running agent. For each one, write down: what file it produces, where that file lives, and what the most important fields are. This is your collector's input spec.

Step 2: Write the Collector (10 minutes). Customize the Layer 1 template with your actual file paths. Save as ~/dashboard/prompts/collect.md. The collector reads, merges, and writes one JSON file. That is all it does.

Step 3: Write the Prioritizer (10 minutes). Customize the Layer 2 template. A day trader's P0 looks different from a product manager's P0. Tune the rules to match what actually matters in your world. Save as ~/dashboard/prompts/prioritize.md.

Step 4: Chain Them Together. Your daily schedule now looks like this:

• 5:30 AM: Agent 1 runs (research)
• 5:45 AM: Agent 2 runs (financial)
• 6:00 AM: Agent 3 runs (email)
• 6:15 AM: Collector runs
• 6:20 AM: Prioritizer runs
• 7:00 AM: You open one file with your coffee

Step 5: Test the Chain. Run each step manually in sequence. Verify the collector reads all output correctly. Verify the prioritizer ranks items sensibly. Fix prompts until the morning view matches what you would want to see.

Three Upgrades Worth Building

1. "Yesterday's Decisions" tracker. Add a section that records what you decided on P1 items yesterday. After 30 days, you can spot patterns. Did you always ignore the same type of alert? Remove it from the dashboard — it is noise. Did you always act on competitor moves? Elevate those to P0.

2. "Agent Suggestions" section. Tell your prioritizer to include one line at the bottom: "Based on this week's patterns, consider [suggestion]." After 30 days of data, these suggestions get surprisingly good — because the prioritizer sees all your agents' output together and spots cross-domain patterns no individual agent would catch.

3. Freshness check. If any agent output is more than 24 hours old, the dashboard should flag it as P0. Silent agent failures are the most dangerous failure mode — you see yesterday's data and assume nothing changed.

The Compound Effect

Without a dashboard, you check 3 files. Some mornings you skip one. Some mornings you skim and miss a P0 buried in a routine report.

With a dashboard, you check 1 file. Every item is ranked. P0 items are impossible to miss. Your review time drops from 20 scattered minutes to 5 focused minutes.

But the real compounding happens in the prioritizer. After 30 days, it has seen 30 mornings of your data. It learns what "normal" looks like for your metrics. When something deviates, it flags it — even if no individual agent caught it, because the pattern only emerges when you see all outputs together.

Your dashboard becomes smarter than any individual agent because it sees the whole picture.

What Could Go Wrong

1. Over-engineering on day one. Start with collector + prioritizer only. Skip the HTML renderer. Skip charts. Get the ranking right first — that is where all the value is.
2. Too many P0 items. If everything is urgent, nothing is. Calibrate so P0 has 0-2 items on a normal day. More than that means your thresholds are wrong.
3. Stale agent output. If an agent fails silently, the dashboard shows yesterday's data as if it is fresh. The freshness check (upgrade #3 above) prevents this.
4. Dashboard fatigue. If you stop reading it, the value drops to zero. Keep it under 5 minutes. Remove sections you never act on. A dashboard you ignore is worse than no dashboard.

The Bottom Line

You built agents that work while you sleep. Now build the single page that tells you what they found — ranked, filtered, and ready for your 5-minute morning review.

The dashboard is not a nice-to-have. It is the difference between having AI assistants and having AI assistants you actually use.

Try It This Week

If you have one agent: build a collector and prioritizer for just that agent. The dashboard will feel simple — good. When you add a second agent next week, the dashboard's value doubles immediately.

If you have multiple agents: build the full chain. Open the morning view with your coffee on Monday.

Reply to this email with your dashboard setup — I will feature the best ones in a future issue.