Understand Platform

Understand Platform

Quick Start

Use this framing when someone asks what Altertable is:

1. Altertable is the data runtime for the AI era: one governed lakehouse for humans, applications, analytics, and agents.
2. Its foundation combines DuckDB compute, Parquet storage, managed catalogs, external catalogs, and semantic models.
3. Agents and MCP clients use the same governed context to inspect schemas, run SQL, build insights, monitor dashboards, and use knowledge.
4. The platform's core operating loop is: Data -> Insights/Dashboards -> Tasks/Agents -> Notifications/Discoveries -> Review/Feedback -> Memories/Knowledge -> Better Future Analysis.

When to Use This Skill

• User asks "what is Altertable?" or "how does the platform work?"
• User wants the difference between agents, discoveries, and memories
• User asks how insights and dashboards connect to monitoring
• User asks how Altertable differs from traditional warehouse-first stacks
• User needs a conceptual architecture explanation before implementation details

Core Platform Narrative

Most data stacks were optimized for batch pipelines, dashboards, and occasional human queries. Altertable is optimized for always-on analysis where agents continuously consume data.

Use these points in order:

1. Foundation: modern lakehouse architecture with DuckDB workers over Parquet and object storage.
2. Access: managed catalogs, external catalogs, HTTP APIs, SQL adapters, MCP tools, and Ask Agent all query the same data layer.
3. Context layer: semantic models and knowledge entries give agents shared business definitions.
4. Operational output: insights, dashboards, tasks, notifications, and discoveries turn analysis into ongoing workflows.
5. Learning loop: memories retain feedback and improve future agent behavior.

Concept Map

Agents

Autonomous data collaborators that execute both repetitive and higher-level analytics work.

• Synchronize sources and maintain data readiness
• Inspect catalogs, run SQL, and use semantic models
• Build or update queries and visual outputs
• Monitor insights and dashboards continuously
• Generate notifications or discoveries when something noteworthy happens
• Learn from feedback through memories

Discoveries

Reviewable findings generated by agents.

• Include context, rationale, and suggested actions
• Require human approval or rejection
• Can represent anomalies, trend changes, segment shifts, schema/model changes, and event readiness
• Become a primary collaboration interface between agents and teams

Memories

Persistent knowledge accumulated by agents across runs.

• Episodic: what happened
• Semantic: what it means
• Procedural: how to handle it next time
• Reinforced or weakened by discovery review outcomes and repeated use

Knowledge Entries

Durable reference material that the team writes down or syncs from repositories.

• Metric definitions, operating procedures, product terminology, and source caveats
• Used by Ask Agent, MCP clients, and tasks while answering questions
• Complements memories, which are learned from agent runs and feedback

Insights

Persistent analyses and visualizations over lakehouse data.

• Funnel, segmentation, semantic, and SQL insights cover different analysis needs
• Serve as reusable analytical building blocks
• Can be monitored directly by agents

Dashboards

Collections of insights organized for KPI tracking and shared monitoring.

• Aggregate related metrics and context in one place
• Support shared variables for coordinated filtering
• Can have attached agents that watch for anomalies and trend shifts

How the Concepts Work Together

Data ingestion -> Catalogs + lakehouse storage/query engine
    -> Semantic models + knowledge
    -> Insights and Dashboards
        -> Tasks and Agent Monitoring
            -> Notifications and Discoveries
                -> Human Review and feedback
                    -> Memories and knowledge updated
                        -> Better future monitoring and analysis

Communication Guidelines

When explaining the platform:

• Start with outcomes (continuous analysis, faster decisions, lower marginal cost at scale)
• Then map to concepts (agents, discoveries, memories, insights, dashboards)
• Emphasize human-in-the-loop review for quality and trust
• Distinguish "analysis artifacts" (insights/dashboards) from "agent outputs" (discoveries)
• Describe memories as adaptive context, not static storage

Common Pitfalls

• Presenting Altertable as only a BI/dashboard tool
• Describing agents as one-shot assistants instead of continuous collaborators
• Skipping the human review stage in the discovery lifecycle
• Treating discoveries as equivalent to insights (they are not)
• Omitting memory feedback loops when explaining how agent quality improves over time
• Leading with implementation internals before clarifying conceptual flow

Reference Files

• Platform overview
• Agents
• Discoveries
• Memories
• Insights and dashboards