Understanding Platform
Quick Start
Use this framing when someone asks what Altertable is:
- Altertable is an operational data platform built for continuous, agent-driven workloads.
- Its lakehouse foundation combines real-time ingestion, fast columnar analytics, and open standards.
- Agents run continuously on top of this data layer to monitor, model, and analyze data.
- 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:
- Foundation: modern lakehouse architecture with warehouse-grade performance and better economics for high query volume.
- Access: data stays continuously queryable by both humans and software.
- Intelligence layer: agents orchestrate multiple LLMs through an asynchronous job system.
- Operational output: discoveries surface anomalies, trends, and opportunities for human review.
- 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