Opportunities › Fleet orchestration for AI agents › Proposed app
Herdbook
Deploy, isolate and debug fleets of AI agents across teams and channels
- Score
- 48
- confidence 69
- Evidence
- 9
- items on the page
- Rivals
- 3
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
ScreensConcept
Desktop app: Operations and engineering teams monitor dashboards, trace failures, and manage fleet deployments—dense monitoring work that demands keyboard input and large screens. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Fleet board
1 needs attention14 agents
About this appAI proposal
Herdbook is a control room for teams that run many AI agents at once. Group agents into workspaces per team or business unit, deploy the same agent to several channels and accounts, and watch every one from a single phone-friendly dashboard.
It sits above the agent frameworks you already use. It does not change how an agent thinks; it handles where it runs, who can touch it, and what happened when something broke. Every action is tied to one agent, one version and one team.
Built for operations and engineering teams who have outgrown single-agent tools and do not want to maintain a home-made deployment layer.
- Tagline
- Know every agent in your fleet.
- Voice
- CalmDirectPractical
- Why this name
- A herd needs a keeper and a record. Herdbook is the register of every agent you run, plain to say and easy to remember.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Team workspaces with hard boundaries
Each team gets its own space with separate agents, secrets and access. One team's agents cannot read or change another's.
No member yet
One agent, many channels and accounts
Define an agent once and roll it out to several channels and customer accounts, with per-target settings.
No member yet
Fleet board
See every agent's state, version, last activity and error rate in one list. Filter by team, channel or account.
No member yet
Failure trace back to the agent
Follow a failed run to the agent, version, channel and change that caused it.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F4D3ABarn Green · primary#F6F1E7Oat · surface#1B1F1DInk · text#E8A317Marigold · accent#B5482EClay · alert / data
Typography
Aa
Herdbook
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Know every agent in your fleet.
PricingAI proposal
Material shows LangGraph at $39 a month with a free plan, and CrewAI on request for paid plans. Team tier mirrors $39; other tiers are starting points to test.
Free
Free
- 1 workspace
- Up to 3 agents
- 7-day run history
Team
$39 a month
- Up to 3 workspaces
- Up to 15 agents
- Staged rollouts
- 30-day run history
Fleet
$149 a month
- Unlimited workspaces
- Up to 100 agents
- Audit log export
- Resource view
- 90-day run history
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- No loop in the product. It reaches customers through sales, content and community posts. Its use is internal to a company's operations, so output is not shared outward.
- Do they become customers?
- Teams are found by direct outreach and by writing about fleet operations. Test whether teams with about 10 agents pay for a hosted control room.
- First customers
- Engineering leads at AI-native companies running 10 to 30 agents across several customer accounts or channels, on one agent framework.
- Where to reach them
- Hacker News: Follow up with the thread's commenters and post a write-up on scaling past ten agents.
- Direct outreach: Message platform and ops engineers at small AI-native companies, offering a free setup of their fleet.
- First milestone
- Five teams with 10+ agents each paying for the Team tier after a 30-day pilot.
Why people already pay for this
- Public claimThere is active deployment of AI agents at scale in production environments across multiple channels and accounts.
- VerifiedHacker News post received 60 points and 43 comments
- Public claimTeams hit scalability limits with single-agent tools when managing 10+ agents across multiple accounts.
- Public claimOperational management pain includes deployment, monitoring, team isolation, and debugging multi-agent failures.
- Public claim
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Agent runtime connectorLightweight Go agent that runs beside each deployed agentReports status and memory and applies deploys. Source claims a low per-agent footprint matters.
- This app · Telemetry storageTime-series store for run events and metricsFleet board and traces need fast queries over many runs.
RisksAI inference
- TechnicalConnecting to many agent frameworks and channels is a large job; the first version must pick one or two frameworks and a few channels.
- CommercialTeams may build in-house on Kubernetes or wait for framework vendors to add fleet features. Demand beyond the Hacker News thread is unproven.
- DataRun traces may contain customer data. Customers will ask for strong isolation, redaction and retention controls early.
- PlatformFramework vendors could change APIs or ship their own fleet management, breaking connectors or removing the gap.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.