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A Brand75 Framework
ABOS — the framework behind Brand75's AI agent builds.
ABOS is the methodology Brand75 uses to design and build AI agents for service businesses. Each agent has a clear job, scoped rules, and a human checkpoint — so real operations work like sales follow-up, intake, and reporting can be handled by AI inside boundaries you control.
11
Agents in production
5
Lifecycle phases
6
Core operating rules
Human
on the loop
Who this is for
Service-based small businesses already running into "we need more hands" — agencies, law firms, consultancies, contractors. If founder hours are eaten by repeatable knowledge work, ABOS is the design pattern Brand75 uses to give that time back.
What you actually buy first
An AI Pilot — one scoped 2–4 week build of a single agent with a fixed price, on the SalesBridge platform. ABOS is the framework that informs how it's built. You don't buy ABOS itself.
What outcome to expect
A measurable improvement in one workflow — faster first response on inbound leads, fewer dropped follow-ups, less manual ops work. Real numbers, real check-ins. If the pilot doesn't pay back, we say so.
What is ABOS
A design pattern for AI agents that support real business operations.
ABOS — the Agent-Based Operating System — is how Brand75 designs AI agents that go beyond chatbots. Each agent has identity, scoped capabilities, operating rules, knowledge, and a heartbeat. Together they form a small organization with clear reporting lines, accountability, and verifiable output.
It's not a product. It's a methodology, proven across our own agency operations and packaged for service businesses ready to extend their team's capacity without hiring more humans for the repetitive work.
Anatomy of every agent
IdentitySOUL.md
CapabilitiesTOOLS.md
Operating RulesAGENTS.md
Knowledge/knowledge/
HeartbeatHEARTBEAT.md
Reporting5-field format
The Five Components
Anatomy of an AI agent.
Every agent in an ABOS team is built from the same five primitives. Click any card to expand.
I
SOUL.md
Identity
Who the agent is, what scope they own, and where their authority ends.
- Name, role, reporting line
- Personality and tone of voice
- Domain of authority — what they decide vs. escalate
- Hard boundaries — what they never touch
- Self-reported model identity
Click to expand →
C
TOOLS.md
Capabilities
The exact tools, paths, and commands the agent can use to act on the world.
- Real file paths, no abstractions
- API endpoints with required scopes
- CLI commands and arguments
- MCP tools with use cases
- Capped under 10K characters to avoid context overflow
Click to expand →
R
AGENTS.md
Operating Rules
The constraints, reporting format, and verification protocol every agent must follow.
- Verify-before-reporting protocol
- 5-field structured report format
- Status taxonomy (STARTED, COMPLETED, BLOCKED, etc.)
- Escalation paths and thresholds
- Anti-narration rule (no filler updates)
Click to expand →
K
/knowledge/
Knowledge Base
Domain-specific reference material the agent loads on demand.
- Brand voice, ICP, positioning docs
- Service-specific playbooks
- Tool guides and integration notes
- Past decisions and rationale
- Indexed for fast retrieval
Click to expand →
H
HEARTBEAT.md
Heartbeat
The recurring tasks that keep the agent moving without being asked.
- Cron-driven check-ins
- Per-platform monitoring cadence
- Weekly review rituals
- Auto-baselines after activation
- Escalation triggers when thresholds breach
Click to expand →
Lifecycle
Five phases from idea to autonomous worker.
No agent goes live without clearing every phase. Click a phase to see what happens inside it.
1
2
Build
Author the files
3
Validate
Pre-flight & spawn
4
Activate
Announce & baseline
5
Monitor
48h supervised watch
Design
Strategy defines the agent before a single file is written. We answer: what business outcome does this agent own, what's the smallest scope that delivers it, and what would success look like in 30 days?
- Draft role description and reporting line
- Identify upstream and downstream agents
- Write SOUL.md, TOOLS.md skeleton, knowledge index
- Approve scope with the human owner before building
Real Team in Production
An ABOS team running today.
This is the actual agent org chart powering Brand75 operations. Click any agent to see their role.
Orchestrator
Atlas
Routes work, owns context
Strategy / CEO
Ryan
Verification & truth
CMO / Marketing
Koa
Marketing org owner
COO / Operations
Aura
Systems & pipelines
Sales
Nova
Pipeline & outreach
VibeCoder
Kai
Web & product builds
Copywriter
Muse
Brand voice & copy
Social Media
Nalu
Multi-platform monitor
Citations
Iris
Brand intelligence
Builder
Leo
Infra & automation
Scraper
Echo
Data ingestion
Embedded in every agent
Six rules that make autonomy safe.
These rules ship with every ABOS agent. They're the difference between an LLM that hallucinates and an agent you can trust to act unsupervised.
RULE 01
Verify Before Reporting
An agent never claims a task is done. It re-reads the file, runs the check, queries the API, then reports. Trust comes from evidence.
RULE 02
Anti-Narration
No "I'm going to start by…" filler. Agents lead with action and report results. The diff is the proof, not the prose.
RULE 03
Experience Logging
Every non-trivial run writes a structured log entry. Future runs read it. The team learns instead of repeating mistakes.
RULE 04
5-Field Report Format
task_id · status · result · blockers · next_step. Five fields, every time. Parsing is easy and escalation is automatic.
RULE 05
Status Taxonomy
STARTED · COMPLETED · BLOCKED · ESCALATED · DELEGATED. Five states, no ambiguity. The orchestrator routes based on these alone.
RULE 06
Operating Model Lock
Each agent declares its own model identity. Routing changes propagate to the agent's self-report. No drift, no surprises.
When things go wrong
Six failure modes — and how ABOS catches them.
Autonomous systems fail. The framework's job is to make every failure visible, fast.
| Failure Mode | How It Shows Up | The Fix |
|---|---|---|
| Hallucinated completion | Agent reports COMPLETED but artifact is missing | Verify-before-report + Ryan audit pass |
| Context overflow | TOOLS.md or AGENTS.md exceeds 10K chars | Auto-cap with overflow into PROTOCOLS.md |
| Silent rate-limit | Model 429s mid-task, agent hangs | Health monitor failover to fallback chain |
| Scope creep | Agent acts outside SOUL.md authority | Hard boundaries in identity + escalation |
| Stale knowledge | Memory says X exists, X was renamed | Verify-before-recommend on every memory hit |
| Cascading failure | One agent breaks, queue backs up | Heartbeat health checks + isolated retries |
Why this works
Principles, not magic.
ABOS works because it borrows from how real organizations are built — clear roles, written rules, accountability loops — and translates them into structures an LLM can actually follow.
→
Roles, not prompts
Each agent owns a domain end-to-end. Prompts vanish on cold start; roles persist across sessions, models, and weeks.
⌘
Files, not memory
The team's truth lives in version-controlled files. Memory drifts. Files don't.
∞
Verifiable autonomy
Every agent action produces a check the next agent (or human) can audit. Trust scales because evidence scales.
◊
Composable structure
Add a new agent in days, not weeks. The framework is the same. The role description changes.
▲
Human-on-the-loop
The owner approves scope, reviews escalations, and shapes strategy. Agents handle the work, not the judgment.
●
Built for service businesses
Agencies, law firms, consultancies. Anywhere repeatable knowledge work eats founder hours, ABOS gives that time back.
✦ How to start an ABOS build
Start with an AI Pilot
ABOS is the framework. The fastest way to start is an AI Pilot — one scoped 2–4 week build of your first agent on the SalesBridge platform. Multi-agent teams typically start with the Growth Engine Build for the connected systems layer.
From $3,500–$4,500 fixed2–4 weeks · + SalesBridge $797/mo
Book a call about an AI Pilot
See what's included → · Multi-agent build? Talk to Brand75 →
Ready to build your team?
Brand75 designs and deploys ABOS agent teams for service businesses. We start with a single agent, prove it pays for itself, then scale.
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