Ingestion — How Information Enters the System
Ingestion is where raw inputs become usable signals.
Inputs may include:
- Text (forms, emails, chat)
- Structured data (CRM, ERP)
- Documents (PDFs, PPTs)
- Images and scans
- Audio and voice
- Video transcripts
Ingestion handles:
- Cleaning and normalization
- Security checks
- Metadata tagging
- Chunking large content into meaningful units
- Preparing data for reasoning
If ingestion is sloppy, intelligence downstream becomes guesswork.
Orchestration — How the System Controls Flow
Orchestration is the control layer.
It does not think. It decides what happens next.
Orchestration governs:
- Workflow sequencing
- Conditional branching
- Parallel agent execution
- Error handling and retries
- Human-in-the-loop triggers
- When chunking occurs and which memory is accessed
The orchestra does not play music.
It ensures the right instruments play at the right time.
The AI Brain — Where Reasoning Happens
The AI brain is responsible for thinking, not storing. It:
- Interprets intent
- Evaluates context
- Weighs options
- Assesses confidence
- Decides next actions
The brain reasons with memory — it does not replace it.
Intelligence is not knowing everything.
It is knowing what matters now.
Memory — How Context Is Preserved
Memory allows AI automation to stay coherent and consistent.
Types of memory include:
- Short-term memory (current task or conversation)
- Long-term memory (knowledge bases, history)
- Episodic memory (past interactions and outcomes)
- Operational memory (system state and checkpoints)
Memory supports reasoning and learning — it does not act independently.
Actions — Where AI Automation Touches Reality
Actions turn decisions into outcomes.
Examples include:
- Updating CRM records
- Sending messages or notifications
- Generating documents
- Triggering workflows
- Assigning tasks to humans
- Escalating approvals
All actions are designed to be:
- Logged
- Traceable
- Governed
- Reversible where possible
Output Interfaces — How Results Reach Humans
AI automation must always return to humans clearly.
Output channels include:
- Dashboards
- Chat interfaces
- Reports
- Notifications
- System updates
If an AI system cannot explain its output, it has failed — even if it is technically correct.
Governance and Safety Layer — Control Overlay
Governance overlays the entire architecture.
It enforces:
- Guardrails
- Role-based access
- Approval workflows
- Audit logs
- Compliance constraints
Governance is not a module.
It is a cross-cutting concern.
Observation and Feedback — How Systems Improve
Observation closes the loop.
Systems continuously monitor:
- Decisions made
- Confidence levels
- Errors and exceptions
- Human overrides
- Real-world outcomes
These signals feed back into orchestration, memory, and guardrails — enabling controlled improvement over time.
