This page tracks the current state of Remyx’s capabilities. We publish it so customers, collaborators, and researchers can see where we’re going well before each capability ships.For the architectural through-line, see Causal Intelligence. For how these capabilities map to the customer journey, see Maturity Progression.
These capabilities are live in production today. They form the Stage 2 foundations: the agent fleet (Agents, Inbox, Reports) plus the ExperimentOps machinery underneath it.
Status
Capability
Description
Shipped
Experiment capture
Full lifecycle from origin through decision, with target metric, hypothesis, and decision rationale captured directly on the experiment record
Shipped
Cross-experiment patterns
Tag-based clustering identifies which directions consistently produce results
Shipped
Resource discovery
Semantic search across papers, repos, models, and datasets, matched to your team’s experiment history
Shipped
History-aware recommendation ranking
The structured shipping history extracted from your repo feeds the recommendation ranker, so candidates that align with your team’s actual trajectory rank higher
Shipped
Preference-model ranking
A per-team preference model fit over past experiments scores candidate work and breaks ties behind relevance
Shipped
Outrider agents
Outrider installed per repo: a scheduled GitHub Action that opens a reviewable draft PR — or a discussion Issue — for the next paper worth implementing; a person reviews and merges
Shipped
Multi-backend agents
Agent runs route at Anthropic (Claude), Z.ai (GLM), or Moonshot (Kimi), including the two-tier drafter/refiner fleet layout
Shipped
Inbox
A derived triage queue of Review / Decide / Fix items that auto-resolves when GitHub does
Shipped
Fleet reports
The fleet in one view: KPIs, deliverable pipeline, contribution grid, per-agent states, and cost per code artifact
Shipped
Agent direction learning
Every merge, skip, and discussion re-weights what each agent proposes next — surfaced as evidence-backed Promoted / Demoted / Held directions
Stage 2. The evidence layer feeding the causal model.
Status
Capability
Description
Planned
Observational log ingestion
Pluggable adapters for Datadog, Honeycomb, structured JSON, OpenTelemetry. Customers connect existing telemetry, and Remyx populates the evidence layer without instrumentation changes
Planned
Commit-correlated regime boundary detection
Extension of the existing repo integration to identify regime changes in the data-generating process from commit history
Planned
Quasi-experiment identification
Combine observational logs with regime boundaries to produce identified causal effects via difference-in-differences, interrupted time series, or regression discontinuity
Planned
Causal discovery
Bootstrap a partial causal graph from observational data, supplemented by regime-boundary structure. Discovered structure is human-validated before being treated as the working model
Planned
Causal graph engine
Versioned causal graph as a top-level object, supporting interventional, counterfactual, and mediation-aware graphical models. Semi-Markovian formulation to handle latent confounders
Planned
Causal data fusion
Combine evidence from multiple sources into one coherent posterior. Conflict resolution, graph refinement proposals, continuous incremental updates
Given a question, classify it by required identification layer, route to evidence sources with appropriate identification, and dispatch to estimation logic
Planned
Natural language query layer
Customer-facing interface that takes natural language questions, parses into formal estimands, and returns natural language answers with identification status and recommendations
Python SDK that wraps decision points in your AI system. The first version captures natural policy output without applying overrides
Planned
Shadow-mode audit infrastructure
Dedicated product surface for the shadow-mode adoption phase. Audit trail viewer, override proposal review, and compliance reporting
Planned
CTF-RAND override policies
Extension of the SDK with counterfactual randomization. Trajectory-consistent semantics by default. Per-decision-point semantics opt-in for mediation analysis
Planned
ETT and NDE estimation
Counterfactual estimation procedures for effect-of-treatment-on-the-treated and natural direct effect
The triage layer matures with the customer — and it is what the agent fleet’s ranking grows into. Stage 2 customers see quasi-experimental recommendations. Stage 3 customers see A/B test recommendations. Stage 4 customers see CTF-RAND recommendations.
Status
Capability
Description
Planned
Hypothesis ranking and evidence path recommendation
Rank hypotheses by expected information gain. Identify the cheapest evidence path to an answer for each
Planned
Orchestration scheduler
Coordinate active interventions (CTF-RAND randomizations, A/B tests, quasi-experiment analyses) for maximum concurrency without compromising estimate validity
Planned
Identification-enabling intervention proposals
Proactively propose CTF-RAND or A/B interventions that would make currently-unidentifiable hypotheses estimable
Foundational evidence-layer capabilities ship first. The causal model and query layer come next. A/B integration follows. Counterfactual perturbations ship last.The dependency hot path through the architecture follows this order.
Evidence schema.
Observational log ingestion.
Quasi-experiment identification.
Causal graph engine and data fusion.
Identification dispatcher and natural language query.
A/B integration.
Shadow-decision SDK.
CTF-RAND override policies.
ETT and NDE estimation.
This roadmap is shared publicly. We update it as priorities shift.
