Understanding Platform

Quick Start

Use this framing when someone asks what Altertable is:

1. Altertable is an operational data platform built for continuous, agent-driven workloads.
2. Its lakehouse foundation combines real-time ingestion, fast columnar analytics, and open standards.
3. Agents run continuously on top of this data layer to monitor, model, and analyze data.
4. The platform's core operating loop is: Insights/Dashboards -> Agent Monitoring -> Discoveries -> Human Review -> Memories -> 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 warehouse-grade performance and better economics for high query volume.
2. Access: data stays continuously queryable by both humans and software.
3. Intelligence layer: agents orchestrate multiple LLMs through an asynchronous job system.
4. Operational output: discoveries surface anomalies, trends, and opportunities for human review.
5. Learning loop: memories retain validated context 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
• Build or update models, queries, and visual outputs
• Monitor insights and dashboards continuously
• Generate 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

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 -> Lakehouse storage/query engine
    -> Insights and Dashboards
        -> Continuous Agent Monitoring
            -> Discoveries
                -> Human Review (accept/reject)
                    -> Memories 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