Technical Deep Dive
Your dashboard runs on an
agent swarm.
Most tools send your request to a single LLM and hope for the best. GlacierHub routes it through a coordinated swarm of specialized agents — each an expert at one slice of the problem, each running the optimal model. The result: deterministic, production-quality dashboards from natural language.
No prompt engineering. No manual API configuration. No single point of failure.
Agent swarm, one pipeline
Each agent solves one slice and passes enriched context to the next. Specialized agents on a shared blackboard turn a probabilistic system into a predictable one.
Intent Analyzer
Extracts structured intent from natural language. Identifies entities (currencies, tickers, metrics), classifies intent type (compare, forecast, correlate), and captures temporal context. Returns confidence scores.
Doesn't know about APIs or charts — just understands what you're asking.
API Hunter
Searches a unified registry of 80+ data providers using two-phase lookup: fast keyword search against 20+ categories, then AI-powered ranking of top matches. Falls back to web search when the registry returns nothing.
Prioritizes free-tier APIs by default. Paid sources are upgrades, not requirements.
Access Evaluator
The first critical guardrail. Evaluates pricing, auth requirements, rate limits. Calculates coverage scores. When multiple viable options exist, the pipeline pauses for user selection — the orchestrator freezes the process state until you respond.
Hard constraint: at least one free option must always be available.
Documentation Parser
Fetches live API documentation via Context7. Resolves library IDs, extracts endpoint specs, maps auth requirements. For providers with OpenAPI specs, auto-parses in 2–5 seconds vs. 15–20 seconds for LLM-based extraction.
Works with current documentation, not cached snapshots from training data.
Endpoint Analyzer
Maps intent to specific API endpoints and parameters. Builds query strings, generates sample responses for validation, stores expected schemas alongside configs for drift detection later.
Understands data structures but knows nothing about visualization.
Chart Recommender
Selects from 27 visualization types — line, bar, area, pie, scatter, candlestick, treemap, sparkline, radar, gauge, and generative UI like comparison grids, ranking lists, and stat cards. For entity-type queries (players, companies, people), recommends rich entity cards with images, stats, bios, and action buttons instead of traditional charts. Validates intent-to-chart alignment: time-series data never lands in a pie chart. Confidence scoring ranks every candidate.
Understands visualization best practices but knows nothing about your data source.
Chart Generator
Synthesizes all outputs into the final widget config: chart type, series definitions, data fetch URLs, transformation paths, refresh intervals. Every output is validated against a strict Zod schema — grid positions, field references, data source constraints — before it leaves this agent. Structural errors are caught here, not downstream.
Sees the complete picture but doesn't make the final call.
Quality Review
Two-phase review. First, four deterministic checks run as pure functions — styling compliance, endpoint validity, schema completeness, and field references — rejecting configs with structural errors before an LLM is ever called. Then three semantic checks use AI: domain alignment, hallucination detection, and entity type matching. Failures produce targeted revision requests routed to the specific agent responsible.
Deterministic where possible, AI where necessary. No wasted tokens on broken schemas.
Durable execution, not serverless timeouts
Every agent runs as a separate durable task. The orchestrator dispatches them in parallel or sequence, coordinated through a shared blackboard. No serverless timeouts — the pipeline runs until the job is done.
Process checkpointing freezes execution state when the pipeline pauses for user input. When you respond, it resumes exactly where it left off. No polling. No timeouts.
Graceful fallback at every step. Non-fatal failures skip optional steps without killing the pipeline. If an API requires auth you haven't configured, the system offers alternatives. Degrade gracefully, never fail catastrophically.
Real-time visibility via server-sent events. See every agent step, elapsed time, and tool call as the pipeline runs — not just a spinner.
Guardrails, not suggestions
Hard constraints at every layer. Not prompt instructions LLMs can ignore — enforced rules agents cannot violate.
Output validation
Every widget config is validated against a strict Zod schema — ChartSpec — at two points: once after generation, once after quality review. Grid overflow, missing series fields, invalid refresh intervals, broken endpoint URLs — all caught by deterministic parsing, not LLM judgment. The pipeline produces a valid widget or fails with an actionable trace.
Free-tier guarantee
The Access Evaluator mandates at least one free option per chart. If every candidate API requires payment, the agent rejects all results and triggers a new search with relaxed constraints. Users build functional dashboards without payment information. Paid APIs are upgrades, not requirements.
Domain classifier
169 pre-defined terms across finance, weather, sports, and health enable instant classification without an LLM call. This cuts latency and cost for the majority of requests. The LLM is only invoked for ambiguous queries — keeping inference costs proportional to complexity.
Schema drift detection
The Endpoint Analyzer stores expected response schemas alongside chart configs. When live data is fetched, actual responses are compared against stored schemas. Mismatches trigger warnings and attempt automatic remapping — catching API versioning issues before they break dashboards.
Parallel by default, sequential when needed
Five search tiers fire simultaneously. Results validate by priority. Inter-phase requests resolve from cached blackboard reports — zero extra LLM calls.
Discovery speed
When the API Hunter starts searching, all five tiers fire at once. The internal registry typically resolves in 200ms. Web search might take 3 seconds. But you only wait for the slowest tier, not the sum of all five.
The orchestrator collects results as they arrive, then validates in priority order. If the registry returns a match, web search results are discarded. If the Scout finds a direct endpoint, the Documentation Parser and Endpoint Analyzer run in parallel too.
Sequential fallback
Some workflows still require sequential execution. When an API requires documentation parsing before data can be fetched, the Endpoint Analyzer waits for the Documentation Parser to complete.
The orchestrator detects these dependencies automatically. Parallel execution for independent tasks. Sequential coordination when one agent's output is another's input. Net effect: discovery takes the time of the slowest tier, not the sum of all tiers.
20+ agents, one intelligence layer
Chart creation is one swarm. Platform-wide, 20+ specialized agents handle discovery, monitoring, enrichment, and alerts.
Data acquisition
API Signup Agent
Autonomously creates API accounts using a headless browser with self-healing locators. Records every step: which fields to fill, which buttons to click, which confirmations to wait for. Subsequent signups execute deterministically, cutting LLM calls by 70–90%.
Data acquisition
Data Source Analyzer
Discovers endpoints, analyzes pricing, determines rate limits, infers schemas, and generates capability reports for any API. Runs as a multi-tool agent — five specialized tools orchestrated in a single task.
Data acquisition
Parser Generator
Generates and validates response parsers for API endpoints. Fetches documentation, samples 3–5 key endpoints, generates parsers, validates against live responses, stores validated parsers in the knowledge graph. EMA confidence scoring stabilizes parsers over time.
Analysis
Chart Q&A Agent
Ask natural language questions about any chart on your dashboard. Accesses the underlying data, runs statistical analysis, and returns answers in context. Understands the chart's data schema, time range, and series configuration.
Analysis
Anomaly Detector
Monitors data streams for statistical anomalies and threshold breaches. Scheduled and event-driven. When a metric deviates beyond configured bounds, triggers alerts through the notification dispatcher with full context.
Intelligence
Cross-Chart Orchestrator
Sees patterns that individual chart agents can't. Detects inverse correlations between charts, suggests shared controls for related datasets, normalizes conflicting date formats, and negotiates shared refresh intervals to avoid exceeding API rate limits.
Every chart runs its own
intelligence layer.
From single agent to coordinated swarm
Revenue charts get Revenue Analysts. Crypto charts get Gas Analysts. As complexity grows, agents spawn sub-swarms — monitors, enrichers, predictors — coordinated by each chart's local orchestrator.
How it works
Right-click any chart on your dashboard. Select Assign Agents. A panel opens showing available agent types: monitors, enrichers, predictors, alerters. Drag them onto your chart. Each one spawns as an independent durable task, coordinated by the chart's local orchestrator.
The chart becomes a hub. Its orchestrator manages the swarm — routing data between agents, resolving conflicts, enforcing rate limits. Agents run on independent schedules. The forecaster might execute hourly. The anomaly sentinel runs continuously. The correlation finder activates when new data sources are connected.
Swarm composition is dynamic. When a forecaster identifies a sudden trend change, it can spawn a root-cause analyzer to investigate — pulling in data from adjacent charts and connected sources. When the analysis completes, the spawned agent terminates. Adaptive team composition based on what the data demands.
Why swarms, not a single agent
Research on multi-agent systems shows coordinated swarms outperform monolithic agents by 13–57% on complex tasks. Monitoring, enrichment, prediction, and alerting are inherently parallel — they don't need each other's output, they need the same underlying data.
A single agent attempting monitoring and prediction and alerting degrades at all three. Specialized agents in a swarm let each one focus on what it does well, using the model best suited to the task. The orchestrator handles coordination and inter-agent communication through the blackboard. This is the architecture pattern behind the most advanced multi-agent systems — applied to every chart on your dashboard.
Gets smarter with every query.
Self-improving AI agents that learn from every interaction
Successful paths earn confidence and skip redundant steps. Failed paths trigger corrections. Faster, cheaper, smarter — trained on your data, not generic examples.
What the Agent Swarm learns
Schema caching
Stores API response structures after the first successful fetch. The Agent Swarm remembers the data shape and goes straight to execution.
Autonomous signup flows
Records every step of autonomous API account creation: fields, buttons, confirmation screens. The LLM-driven exploration phase happens once — every future signup is a cached replay.
Confidence-based routing
Tracks provider reliability over time. The Agent Swarm automatically balances speed and safety based on historical performance.
Self-healing via Skill Writer
When a run fails, the Skill Writer agent fires asynchronously in the background. It analyzes the complete failure trace — which agent failed, why, what the input was, what constraints were violated — and writes corrective rules directly to that agent's skill knowledge file. The swarm learns from its mistakes and self-corrects across future runs.
Field semantics mapping
Builds a knowledge graph of which fields belong to which data domains. Once the swarm learns that "PTS" means points in basketball and "Pts" means points in motorsport, it never confuses them again.
Provider optimizations
Rate limit patterns, pagination styles, authentication quirks — all captured and reused. Each API gets easier the more you use it.
Data connectivity
Plug in any data source. Agents handle the rest.
80+ providers today, unlimited tomorrow. GlacierHub's data mesh builds on two open standards defining how AI connects to the world.
Model Context Protocol (MCP) — Anthropic's open standard — gives agents a universal interface to external tools and data via JSON-RPC. 100+ MCP server definitions tracked.
Agent-to-Agent Protocol (A2A) — Google's open standard — enables agents across systems to discover each other, negotiate capabilities, and exchange tasks.
The end state: adding a data feed is as simple as pointing at an MCP server. Agents discover capabilities, generate parsers, and validate schemas automatically.
Stripe Revenue API
REST · OAuth2 · 3 charts connected
PostgreSQL Analytics
MCP Server · Direct SQL · 5 charts
Google Analytics
MCP Server · OAuth2 · 2 charts
External Analysis Agent
A2A Protocol · Agent Card discovery
Composable intelligence
Build your own
agent teams.
Enable or disable agents per run. Override models per agent. Topologies from sequential to fully parallel. Customization is opt-in.
Multi-model routing
Each agent can run on a different AI model. Anthropic for reasoning-heavy review, fast models for field classification, open-source models via OpenRouter for cost-sensitive discovery. Access 500+ models with zero markup. Per-agent overrides in a single config — no code changes.
Configurable topology
Three execution topologies: sequential (each agent waits for the previous), parallel-discovery (independent agents fire simultaneously), or custom phase ordering with explicit agent groupings. The orchestrator respects your topology and handles dependency resolution automatically.
Blackboard communication
All agents share a structured blackboard — reports, field semantics, data snapshots. Agents post requests to peers between phases: "what fields are available?", "is this endpoint valid?". The orchestrator resolves requests from cached reports without spawning new tasks. Research shows this pattern improves complex task performance by 13–57% over hub-and-spoke coordination.
Supervisor
Revenue Coordinator
Decomposes queries, routes to specialists
Specialist
MRR Tracker
Stripe + billing integration
Specialist
Churn Predictor
Usage patterns + engagement data
Specialist
Growth Analyst
Marketing spend correlation
Ask: “Why did revenue drop 12% last week?” → The coordinator routes to all three specialists in parallel. MRR Tracker checks for billing anomalies. Churn Predictor identifies accounts at risk. Growth Analyst correlates with campaign changes. The coordinator synthesizes findings into a single answer with supporting charts.
Production-grade, not demo-ware
Durable execution. Process-level checkpointing. Multi-model routing. Deterministic validation. Built for real workloads.
Process checkpointing
When an agent pauses, the execution engine freezes the entire process state — memory, registers, file descriptors — and saves it to disk. Compute is freed. When conditions resolve, execution resumes exactly where it stopped. No polling, no timeouts, no state recreation.
Idempotency
Every task receives a unique idempotency key based on its input hash. If the same request runs twice — retry, user error, infra failure — the engine returns cached results without re-executing. No duplicate API calls, no wasted LLM tokens.
Exponential backoff
Transient failures (503s, network timeouts) retry up to three times with increasing delays. Permanent failures (invalid keys, 404s) fail fast. Built into the orchestration layer — individual agents don't need custom retry logic.
Distributed tracing
Every agent execution links to the parent request via trace IDs. When a build fails, trace through the entire pipeline: which agents ran, how long each took, what inputs they received, what outputs they produced, and exactly where the failure occurred.
Immutable versioning
Each deployment creates a locked version. Running tasks continue on the version they started with. Deploy new agent logic without interrupting active pipelines. No migration scripts, no version conflicts.
Streaming progress
The orchestrator emits metadata updates after each agent completes. The frontend subscribes via server-sent events and renders live progress. Tool history, elapsed time, current step — all visible in real time.
Get Started
See the agents in action
Describe what you want to track. Watch a multi-model swarm build your dashboard. Configure which agents run, what models they use, and how they coordinate.
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