They can be combined into complex workflows or used individually.

3.3.1 Data & Knowledge Agents

These agents form the foundational intelligence layer for data acquisition, normalization, semantic access, and enterprise knowledge modeling within ChordianAI. They ensure that upstream orchestration, analysis, and optimization layers operate on high-integrity, context-rich, and semantically unified data.

List Builder

Status: Active
Function Class: Multi-Source Entity Consolidation

Primary Role: Structured compilation and harmonization of enterprise entities across heterogeneous data sources.

The List Builder aggregates and normalizes entity records—such as suppliers, customers, assets, product lines, facilities, or projects—from multiple internal and external systems. It resolves inconsistencies, aligns schemas, deduplicates entries, and produces a consolidated master list aligned with enterprise standards.

It is designed for organizations that maintain fragmented records across ERP, procurement, finance, CRM, and operational platforms. The agent acts as a controlled consolidation engine, ensuring downstream analytics, optimization, and orchestration processes operate on accurate and unified canonical lists.

The List Builder is particularly critical for workflows involving procurement rationalization, asset utilization, spend consolidation, risk assessment, and vendor governance.

Input Types

• Multi-system data extracts (ERP, procurement, CRM, finance)
• Semi-structured tables (CSV, Excel, reports)
• Asset inventories, contract registries, vendor lists
• Source metadata for deduplication and schema alignment

Output Types

 • Canonical entity lists (suppliers, assets, customers, etc.)
• Harmonized tables with unified schema
• Source-to-target mapping rules
• Deduplication and consistency reports
• Confidence scores for merged records

Representative Enterprise Use Cases by Department

Procurement
• Consolidation of global supplier portfolios across regions and systems.
• Unified vendor master to support sourcing, compliance, and contract management.

Finance
• Harmonized list of cost centers, internal entities, billing accounts, or assets.

Operations / Supply Chain
• Aggregated list of logistics partners, carriers, production assets, or facilities.

Risk & Compliance
• Creation of unified counterparty lists for due diligence and monitoring.

Knowledge Graph

Status: Active
Function Class: Enterprise Relationship Modeling
Primary Role: Construction of a semantic graph mapping relationships, dependencies, and interactions across enterprise entities and datasets.

The Knowledge Graph agent transforms raw and structured data into a unified graph representation that captures the relational fabric of the enterprise. It models dependencies among systems, processes, assets, contracts, sensors, people, applications, and operational events.

This graph serves as a foundational substrate for complex reasoning tasks, cross-domain analytics, anomaly detection, root-cause analysis, regulatory compliance, and optimization at scale. It enables ChordianAI agents to operate with full contextual awareness by providing a relationship-aware knowledge structure.

The agent is essential for organizations requiring integrated views of their infrastructure, operations, supply networks, or contractual ecosystems.

Input Types

 • Structured datasets from ERP, CRM, EAM, and ITSM platforms
• Graph-suitable relationships (parent-child, dependency, ownership, lineage)
• Event logs, sensor metadata, maintenance records
• Contractual entities and associated performance obligations

Output Types

 • Fully formed enterprise knowledge graph
• Relationship schemas and dependency mappings
• Entity linkage confidence metrics
• Domain-specific subgraphs (assets, contracts, supply chain nodes, etc.)
• Graph metadata for downstream semantic search and optimization

Representative Enterprise Use Cases by Department

Operations & Engineering
• Linking machinery, sensors, and maintenance schedules into dependency networks.
• Mapping failure propagation paths for resilience analytics.

Compliance & Risk
• Connecting contractual terms to operational processes and SLA performance.
• Modeling data lineage for regulatory audits.

R&D / Product Development
• Knowledge mapping across experiments, materials, test results, and designs.

IT / Enterprise Architecture
• Dependency graph of applications, APIs, integrations, and data pipelines.

AI Search

Status: Active
Function Class: Enterprise Semantic Retrieval
Primary Role: Unified semantic and keyword search across structured, semi-structured, and unstructured data assets.

AI Search provides a high-precision semantic retrieval layer over enterprise data. It indexes documents, tables, logs, metrics, system entities, KPIs, and knowledge graph nodes to enable language-driven access to information across disparate systems.

This agent supports both user queries and machine-level retrieval inside workflows. It enables ChordianAI to locate relevant information without requiring users or agents to know system-specific schemas, locations, or file structures.

AI Search is a central component for investigations, audit workflows, troubleshooting, anomaly detection, knowledge discovery, and cross-departmental analytics.

Input Types

 • Natural-language queries
• Contextual search prompts from agents
• Structured search filters
• Knowledge graph embeddings
• Historical queries and relevance signals

Output Types

 • Ranked search results across data modalities
• Extracted sections of documents or logs
• Multi-source result aggregation
• Relevance and confidence scoring
• Retrieval context for downstream agents

Representative Enterprise Use Cases by Department

All Departments
• Enterprise-wide search for documents, reports, logs, KPIs, contracts, and system entities.

Support & Investigations
• Cross-system retrieval of customer issue details, incidents, and historical cases.

R&D
• Semantic aggregation of research materials, experiments, or technical documents.

Operations / IT
• Rapid retrieval of log fragments, error sequences, or configuration history.

3.3.2 Analytical & Predictive Agents

These agents provide ChordianAI’s analytical, statistical, forecasting, and decision-simulation capabilities. They generate quantitative insight, identify structural drivers, detect emerging risks, and support strategic scenario evaluation.

UniForecaster

Status: Active
Function Class: Predictive Modeling & Time-Series Forecasting
Primary Role: Generates high-accuracy forecasts for demand, churn, cost, usage, failures, or operational KPIs using hybrid statistical + ML methods.

UniForecaster is ChordianAI’s dedicated sequential prediction engine. It integrates classical time-series methods, machine-learning models, and domain-specific hybrid architectures to produce highly accurate, multi-horizon forecasts across financial, operational, supply-chain, and product domains.

It processes structured historical data, enriches it with contextual signals (seasonality, anomalies, exogenous variables, knowledge-graph attributes), and outputs forecast trajectories required by downstream optimization and orchestration workflows.

The agent supports:
• multivariate time-series prediction
• probabilistic forecasting and confidence intervals
• long-horizon forecasting for capacity and budget planning
• short-horizon predictive signals for real-time control
• anomaly-aware forecasting and regime-shift detection

UniForecaster is optimized for enterprise-scale datasets, irregular sampling patterns, and multi-entity forecasting scenarios (e.g., forecasts per SKU, per region, per machine, or per cost center).

Input Types

 • Historical time-series datasets (cost, demand, revenue, sensor metrics, volume)
• Exogenous features (weather, holidays, KPIs, events)
• Aggregated knowledge-graph features
• Domain constraints (budget, time limits, forecast granularity)
• Organizational metadata (segments, clusters, hierarchies)

Output Types

 • Point forecasts and confidence intervals
• Multi-horizon prediction tables
• Forecast quality metrics and diagnostics
• Anomaly-aware adjusted forecasts
• Derived predictive features for optimization workflows

Representative Enterprise Use Cases by Department

Finance
• Revenue forecasting and cost projection
• Budget cycle prediction with uncertainty quantification

Supply Chain
• Multi-node demand forecasting
• Lead-time prediction and volume planning

Operations
• Machine failure prediction for preventive maintenance
• Workforce demand prediction

Product & Growth
• User churn forecasting
• Feature adoption forecasting

Optimizer Engine

Status: Request Access
Function Class: Mathematical Optimization & Hybrid Solver Framework
Primary Role: Computes optimal allocations, schedules, routing plans, and resource assignments using classical, heuristic, or hybrid (ML + solver) techniques.

The Optimizer Engine is ChordianAI’s computational decision engine for solving deterministic and combinatorial optimization problems. It transforms structured optimization tasks into formal mathematical models and executes them through a combination of:
• linear and mixed-integer programming solvers
• constraint programming
• metaheuristics (e.g., evolutionary algorithms, simulated annealing)
• hybrid ML-guided search strategies
• optional quantum-assisted QUBO solvers

The agent supports multi-objective optimization, feasibility verification, constraint enforcement, and trade-off evaluation across competing business objectives.

It is designed for large-scale enterprise use cases such as workforce scheduling, supply chain routing, budget allocation, asset positioning, energy optimization, and cost minimization workflows. The Optimizer Engine can operate independently or as part of larger orchestration loops managed by the Orchestrate Layer.

Input Types

 • Formal optimization problem (objective functions, constraints, variable definitions)
• Forecasted demand or risk signals (from UniForecaster)
• Resource availability and allocation limits
• Cost parameters, latency constraints, SLA requirements
• Domain-specific configuration templates

Output Types

 • Optimal or near-optimal allocation plans
• Feasible schedules, routes, or configuration sets
• Objective function scores and performance metrics
• Sensitivity analysis and trade-off evaluation
• Constraint satisfaction and feasibility reports

Representative Enterprise Use Cases by Department

Operations
• Workforce and maintenance scheduling
• Asset and facility utilization optimization

Supply Chain
• Multi-echelon routing
• Vehicle and shipment scheduling
• Inventory positioning

Finance
• Budget allocation
• Portfolio optimization with constraints

HR
• Shift optimization and labor allocation

Data Analyzer

Status: Active
Function Class: Statistical Analysis & Pattern Discovery
Primary Role: Applies analytical techniques to uncover correlations, regressions, patterns, and structural drivers of performance across datasets.

The Data Analyzer performs statistical evaluation and feature-level diagnostic analysis across enterprise datasets. It identifies explanatory variables, quantifies relationships, detects latent patterns, and extracts signals relevant to operational, financial, or R&D workflows.

It supports:
• regression analysis
• correlation networks
• feature importance ranking
• clustering and segmentation
• anomaly scoring
• distributional and variance analysis

These analyses feed the Analyzer Layer, Optimizer Engine, and orchestration workflows by providing structured insights about the relationships governing enterprise KPIs.

Input Types

• Structured datasets (financial, operational, product usage, IoT)
• Knowledge graph–enhanced features
• Extracted tabular or aggregated metrics
• Target KPIs or dependent variables

Output Types

• Correlation matrices and dependency maps
• Regression coefficients and statistical significance
• Feature attribution and impact scoring
• Clusters, segments, and behavioral groups
• Pattern summaries for downstream workflows

Representative Enterprise Use Cases by Department

Finance
• Drivers of revenue variance or margin erosion
• Root cause analysis of spend anomalies

Operations
• Identification of leading indicators of downtime or delays

R&D
• Experimental or test result clustering
• Multivariate correlations in performance metrics

Anomaly Detector

Status: Request Access
Function Class: Real-Time Anomaly Detection & Early Warning
Primary Role: Identifies deviations from normal operating behavior, emerging risks, abnormal sensor activity, financial irregularities, or operational failures.

The Anomaly Detector applies statistical, machine learning, and hybrid detection techniques to identify irregular patterns within high-volume or mission-critical datasets. It detects early signals of breakdowns, failures, fraud, outages, cost spikes, and other deviations from expected behavior.

The agent supports:
• real-time anomaly scoring
• contextual anomaly detection (using knowledge graph context)
• seasonality-aware anomaly detection
• multivariate anomaly detection for correlated metrics
• alert generation and automated escalation into Orchestrate Layer workflows

It is designed for enterprise domains where early detection has material financial or operational impact.

Input Types

 • Real-time or batch operational metrics
• Sensor streams and IoT telemetry
• Transaction or cost logs
• Forecast baselines and expected ranges
• Historical “normal” behavior patterns

Output Types

 • Anomaly flags and severity scores
• Explanations or contributing factors
• Risk alerts and recommended next steps
• Time-series segments with abnormal behavior
• Inputs for root-cause analysis workflows

Representative Enterprise Use Cases by Department

Operations
• Real-time detection of process deviations, equipment anomalies, or pipeline failures.

Finance
• Spend anomalies in cloud billing, procurement, or transactional flows.

Manufacturing
• Sensor-level abnormal vibration, thermal deviation, or torque irregularity.

Scenario Simulator

Status: Request Access
Function Class: Strategic Simulation & Sensitivity Analysis
Primary Role: Evaluates alternative decisions, strategies, or environmental conditions to determine potential outcomes and risks.

The Scenario Simulator provides forward-looking, multi-scenario evaluation capabilities. It models alternative strategic decisions, operational plans, or environmental conditions and computes their projected outcomes using linked forecasting, optimization, and analytical components.

It supports:
• multi-scenario financial forecasting
• supply chain disruption simulations
• sensitivity analysis for price, demand, or capacity changes
• resilience evaluation under adverse events
• long-term planning and capital allocation models

The agent integrates probabilistic forecasts, deterministic constraints, and domain assumptions to produce quantitative scenario outcomes used by decision-makers.

Input Types

• Base-case forecasts from UniForecaster
• Parameter variations (price, demand, lead-time, costs, capacity)
• Alternative operational strategies
• Constraint sets and business rules
• Environmental or macroeconomic assumptions

Output Types

• Scenario outcome matrices
• KPI deltas across alternative strategies
• Sensitivity heatmaps and elasticity measures
• Risk-adjusted projections
• Recommendations for optimal strategic choice

Representative Enterprise Use Cases by Department

Strategy & Executive Planning
• Long-range financial scenario modeling
• Assessing outcomes of strategic initiatives

Finance
• Sensitivity analysis for cost structures, pricing changes, or budget changes

Operations / Supply Chain
• Predictive modeling of disruptions, capacity shifts, and routing alternatives

3.3.3 Automation & Action Agents

These agents are for triggering actions, initiating decisions, and enabling enterprise-grade extensibility. They convert analytical and optimization outputs into operational impact through controlled, auditable, and automated actions.

Alert Agent

Status: Request Access
Function Class: Real-Time Notification & Event Triggering
Primary Role: Initiates alerts or system actions when KPIs deviate from expected boundaries.

The Alert Agent provides automated, real-time monitoring and alerting capabilities across ChordianAI workflows. It evaluates KPIs, forecasts, anomaly scores, and operational thresholds and triggers controlled notifications or system actions when deviations occur.

The agent supports:
• threshold-based alerts
• anomaly-triggered notifications
• compound logic (e.g., multi-metric alerts)
• integration with communication systems and incident platforms
• automatic escalation workflows
• fail-safe triggers for high-risk events

This agent is critical for operational resilience, compliance monitoring, production systems oversight, and automated incident response.

Input Types

• KPI streams and monitored metrics
• Forecast deviations and anomaly signals
• Threshold definitions and escalation rules
• Integration endpoints (Slack, OpsGenie, ServiceNow, Teams, SMS)

Output Types

• Real-time notifications
• Escalation messages to operational teams
• Triggered follow-up workflows (e.g., re-run optimization, open incident ticket)
• Logs of all alert events and triggered actions

Representative Enterprise Use Cases by Department

Operations
• Trigger alerts when production equipment deviates from expected ranges.
• Notify teams about pipeline failures or downtime risks.

Support / Customer Success
• Trigger escalation workflows on SLA violations or incident patterns.

Compliance & Risk
• Monitor contractual or regulatory thresholds and trigger interventions.

Decision Agent

Status: Request Access
Function Class: Decision Recommendation & Policy Guidance
Primary Role: Converts analytical outputs into actionable, context-aware decision recommendations.

The Decision Agent evaluates outputs generated by forecasting, optimization, analysis, and anomaly detection agents and synthesizes them into structured, actionable recommendations. It operates as ChordianAI’s policy and next-step engine, guiding users and downstream workflows on the most effective actions given current conditions.

This agent supports:
• next-best-action recommendations
• policy enforcement and decision alignment
• trade-off evaluation across competing objectives
• resource reallocation suggestions
• escalation of decisions into automated workflows
• human-in-loop decision support

The Decision Agent ensures that insights produced by ChordianAI translate directly into operational effect.

Input Types

• Optimization results and objective scores
• Forecast data and confidence intervals
• Risk or anomaly indicators
• Policy rules, business constraints, and priority matrices
• User or department-specific decision models

Output Types

• Action recommendations (ranked by impact or feasibility)
• Policy-aligned decision summaries
• Next-step workflow triggers
• Structured decision rationale for audit logs

Representative Enterprise Use Cases by Department

Operations
• Recommend resource reallocation or shifts based on predicted demand.

Finance
• Suggest budget redistribution or cost-mitigation actions.

Product / Growth
• Recommend retention strategies based on churn risk.

Supply Chain
• Suggest route adjustments or safety stock changes.

Custom Agent SDK

Status: Active
Function Class: Extensibility & Custom Module Integration
Primary Role: Enables enterprises to build proprietary agents, integrate internal ML models, and connect private APIs directly into the ChordianAI orchestration layer.

The Custom Agent SDK provides a controlled development framework that allows enterprises to extend ChordianAI with internal capabilities. It supports creating custom agents that can:
• incorporate proprietary ML models
• integrate with internal APIs, microservices, or data platforms
• apply domain-specific logic
• encapsulate in-house algorithms or decision systems
• embed specialized optimization or simulation code
• reinforce organizational IP within the ChordianAI ecosystem

Custom agents participate seamlessly in ChordianAI workflows, obeying orchestration rules, governance policies, dependency structures, and observability standards.
This SDK ensures that organizations can preserve competitive advantage while operating within ChordianAI’s unified orchestration architecture.

Input Types

• Custom model logic (Python, containerized services, API endpoints)
• Input schemas and agent definitions
• Enterprise authentication and system credentials
• Environment configuration and policy constraints

Output Types

• Execution outputs defined by custom logic
• Integrated workflow node compatible with all ChordianAI layers
• Logs, audit trails, and metadata aligned with platform standards
• Error-handling and fallback signaling

Representative Enterprise Use Cases by Department

Engineering / Platform Teams
• Integrate internal ML inference systems or proprietary tools.
• Wrap microservices or internal APIs into ChordianAI agents.

Data Science
• Deploy proprietary models (forecasting, classification, optimization) as reusable agents.
• Embed experimental algorithms into operational workflows.

IT / Enterprise Architecture
• Connect internal databases, business systems, or legacy engines directly into ChordianAI workflows.

3.3.4 Compute & Infrastructure Agents

These agents deliver ChordianAI’s compute optimization, hardware routing, and next-generation quantum execution capabilities. They ensure that enterprise workloads are executed with optimal cost-efficiency, compute performance, and architectural versatility.
They serve as the operational backbone for organizations requiring scalable AI, large-model efficiency, and hybrid classical–quantum optimization capabilities.

Compression Manager

Status: Request Access
Function Class: Model Compression & Computational Efficiency
Primary Role: Reduces computational overhead of AI models through structured compression, enabling cost-efficient, high-performance inference on enterprise infrastructure.

The Compression Manager applies advanced model compression techniques—including tensor-train decomposition, low-rank approximations, pruning, quantization, distillation, and parameter sharing—to reduce the size, memory footprint, and compute requirements of AI models deployed in ChordianAI workflows.

This agent is designed for enterprises operating large-scale AI systems, LLM-heavy pipelines, or computationally intensive workloads where inference cost, latency, or infrastructure constraints are critical.

Key capabilities include:
• model size reduction without significant accuracy loss
• latency optimization for real-time workflows
• compute cost minimization (GPU/CPU footprint)
• model reuse across teams and environments
• compatibility enforcement for edge, on-prem, or restricted environments
• automatic recommendation of compression strategies based on usage patterns

The Compression Manager ensures AI models remain operationally feasible even in heavily regulated, resource-limited, or cost-sensitive environments.

Input Types

• Pretrained AI/ML models (LLMs, forecasting models, classification/regression models)
• Target deployment environment (edge, GPU, CPU, on-prem, air-gapped)
• Cost, latency, and performance constraints
• Accuracy thresholds and acceptable degradation budgets
• Model usage logs and workload frequency

Output Types

• Compressed model artefacts
• Compression strategy reports
• Performance/accuracy trade-off metrics
• Deployment-ready model versions (quantized, pruned, TT-compressed)
• Model registry entries with lineage and metadata

Representative Enterprise Use Cases by Department

DevOps / Platform Engineering
• Reduce GPU consumption for high-volume inference workloads.
• Enable deployments to CPU-only environments.

Finance / FinOps
• Lower recurring cloud AI spend via model compression.
• Improve ROI of AI workloads with measurable cost reductions.

Product Engineering
• Deploy responsive, low-latency models inside customer-facing apps.
• Support offline or limited-infrastructure deployments.

Hybrid Compute Router

Status: Active
Function Class: Adaptive Compute Selection & Workload Routing
Primary Role: Dynamically routes AI and computational tasks to the most cost-optimal and performance-optimal compute resource (CPU, GPU, LLM endpoint, quantum backend).

The Hybrid Compute Router intelligently distributes workloads across heterogeneous compute environments. It evaluates task characteristics, performance requirements, cost constraints, latency deadlines, and model preferences to determine the optimal execution environment at runtime.

It enables enterprises to balance cost, performance, and reliability while using mixed compute infrastructure or multi-LLM ecosystems.

Capabilities include:
• dynamic routing between CPU, GPU, and GPU-classes (A10, A100, H100, etc.)
• selection among LLMs based on cost/latency/accuracy thresholds
• hybrid classical–quantum execution routing
• enforcement of cost guardrails and compute thresholds
• fallback and failover routing when primary compute is unavailable
• live monitoring of compute performance and real-time re-routing

The agent is essential for controlling AI/ML costs, optimizing inference throughput, and ensuring SLA-compliant execution across distributed and hybrid environments.

Input Types

• Workload metadata (shape, compute intensity, required precision)
• Performance constraints (latency, throughput, SLA)
• Cost caps and budget guardrails
• Allowed compute targets (CPU/GPU/LLM/Quantum)
• Historical performance metrics

Output Types

• Compute routing decisions
• Real-time execution logs
• Cost-performance optimization reports
• Fallback routes and exception handling events
• Model selection metadata for auditability

Representative Enterprise Use Cases by Department

DevOps / Cloud Engineering
• Route inference workloads to cheaper GPUs during low-traffic periods.
• Shift workloads to CPU or alternative models for cost reduction.

Finance / FinOps
• Enforce AI budget limits by dynamically switching to cost-optimal compute paths.

Product & Platform Teams
• Improve response times for user-facing AI applications.
• Route heavy operations to background GPU clusters.

Quantum Solver Connector

Status: Request Access
Function Class: Hybrid Quantum Optimization Interface
Primary Role: Connects ChordianAI to quantum optimization backends (D-Wave, IonQ, Rigetti) for solving hard combinatorial, routing, and scheduling problems.

The Quantum Solver Connector provides a unified abstraction layer for interfacing with leading quantum hardware providers. It translates classical optimization formulations—typically QUBO (Quadratic Unconstrained Binary Optimization) or Ising models—into quantum-compatible representations and orchestrates hybrid classical–quantum optimization cycles.

Supported paradigms include:
• quantum annealing (D-Wave)
• trapped-ion QAOA variants (IonQ)
• superconducting gate-based hybrid solvers (Rigetti)

The agent coordinates:
• QUBO construction from enterprise constraints
• mapping and embedding into quantum hardware topology
• hybrid optimization loops combining classical pre/post-processing
• translation of quantum solutions back into enterprise decision structures

It is designed for use cases where classical solvers struggle due to dimensionality, rugged search spaces, or complex constraint structures.

Input Types

• Optimization problems convertible to QUBO/Ising form
• Classical solver warm-starts
• Constraint matrices and penalty formulations
• Hardware backend selection policies
• Quantum execution budgets and parameter settings

Output Types

• Quantum-optimized solutions
• Multiple candidate solutions with energy scores
• Embedding and mapping diagnostics
• Hybrid solution refinement outputs
• Solver performance and wall-clock metrics

Representative Enterprise Use Cases by Department

R&D / Advanced Analytics
• Research into quantum-accelerated optimization for large, complex problem classes.

Logistics & Supply Chain
• High-complexity routing and scheduling with many interacting constraints.
• Multi-depot or long-horizon delivery optimization under combinatorial explosion.

Operations
• Facility layout and machine scheduling under hard constraints.
• Combinatorial planning with large feasible regions.