Decision-Aware Agent Orchestration
The only agent framework where every AI action passes through governance gates, carries decision context, and produces audit-ready evidence
Today's AI agents are fire-and-forget black boxes: no guardrails, no memory, no accountability. DecisionLedger agents reason through your decision frameworks, reference your organizational history, and pass through governance gates on every action. Every run governed. Every cost tracked. Every outcome calibrated against predictions.
Challenges We Solve
Governance gaps that structured controls and audit trails eliminate.
Agents Run Without Guardrails
Autonomous AI execution with zero governance oversight. One bad recommendation becomes a compliance incident, a reputational risk, or a seven-figure loss. Governance gates enforce policy checks before every agent action so nothing slips through unchecked.
Every Run Starts from Scratch
Agents have no memory of prior decisions, past outcomes, or organizational context. They re-derive what your team already knows. Decision Graph gives agents institutional memory - prior decisions, linked scenarios, and calibrated outcome data flow into every run.
No Audit Trail for AI Actions
When regulators ask who authorized the AI's recommendation and what data informed it, you scramble. Every agent run is logged with full provenance: inputs, outputs, governance metadata, model version, and cost breakdown - audit-ready from day one.
Agent Costs Are a Black Box
No idea what agents cost per run, per model, or per tenant. Budgets blow up without warning. Cost metering tracks every token, every API call, every compute minute - with real-time dashboards and threshold alerts before spend spirals.
Agent Pipeline Anatomy
Every agent pipeline follows a four-stage lifecycle. Each stage is governed, logged, and traceable.
Ingest
Agents pull data from HRIS, financials, CRM, and the Decision Graph. Context is assembled automatically.
Reason
Each agent runs its decision model against the assembled context. Prior outcomes and organizational memory inform every calculation.
Govern
Governance gates enforce policy checks, approval thresholds, and compliance rules before any action is taken.
Act & Record
Approved actions execute with full provenance. Inputs, outputs, costs, and governance metadata are logged for audit.
Use Cases
Real governance scenarios powered by DecisionLedger.
Deploys a 3-agent vendor evaluation pipeline: scoring agent evaluates suppliers on 12 criteria, risk agent flags single-source concentration issues, and compliance agent verifies regulatory requirements. A governance gate requires VP approval for any contract exceeding $500K before the recommendation is finalized.
Cut vendor selection cycle from 3 weeks to 2 days with full audit trail and zero compliance exceptions
Builds an attrition early-warning workflow with Digital Twin integration: data agent pulls live HRIS metrics, risk agent scores flight risk using the organizational digital twin, and recommendation agent suggests retention interventions. Shadow mode validates predictions against actual attrition for 60 days before enabling automated alerts.
Detected 90% of at-risk employees 60 days before resignation with zero false positives escalated
Runs compliance agents in shadow mode against all active decision workflows. Agents flag policy violations, missing approvals, and audit gaps without disrupting operations. Weekly review sessions compare shadow findings to manual audit results before enabling enforcement mode.
Identified 23 policy gaps in first shadow mode cycle, zero audit findings at next external review
Deploys a cost governance agent that monitors AI spend across all agent pipelines in real time. The agent flags budget overruns, identifies underperforming models consuming disproportionate resources, and produces monthly AI cost-benefit reports with ROI breakdowns by department and use case.
35% reduction in AI operational spend within 90 days, with cost-per-decision visibility across every pipeline
Measurable Impact
Based on platform benchmarks across early adopters.
Decision Cycle Time
Governance Coverage
Agent Cost Visibility
Outcome Tracking
Compliance Readiness
Agent Reliability
Full Agent Governance Stack
Everything you need to deploy, govern, and improve AI agents at enterprise scale.
Agent Studio
Design multi-agent pipelines visually. Define system prompts, tool access, model selection, and execution triggers - no code required.
Agent Registry
Register every AI agent with per-agent permissions, activity monitoring, and usage auditing. Know exactly what each agent can and cannot do.
Shadow Mode
Run agents against live production data without executing real actions. Compare shadow outputs to human decisions and validate accuracy before going live.
Kill Switch
Halt any agent instantly - per-agent, per-pipeline, or platform-wide. Sub-second response time, with no delay.
Decision Graph Context
Agents receive your full organizational decision history, linked scenarios, outcome calibration data, and policy constraints as context for every run.
Cost Metering
Track token usage, API calls, and estimated costs per agent, per model, per tenant. Real-time dashboards with budget threshold alerts.
MCP Gateway
Agents connect via Model Context Protocol. Run decision models, create decisions, advance lifecycles, and query the Decision Graph natively.
Outcome Calibration
Record actual outcomes against predictions. Measure agent reliability and prediction accuracy over 30/60/90-day windows with drift detection.
Connects With
Part of 150+ native integrations across CRM, marketing, finance, HR, ecommerce, and analytics
Salesforce
HubSpot
Stripe
Shopify
Google Analytics 4
Workday
QuickBooks
Snowflake
Slack
Zendesk
GitHub
Meta Ads
Mailchimp
NetSuite
Jira
Power BISalesforceHubSpotStripeShopifyGoogle Analytics 4WorkdayQuickBooksSnowflakeSlackZendeskGitHubMeta AdsMailchimpNetSuiteJiraPower BIFeatured Models
Pre-built decision models ready to run with your data.
Scenario Sensitivity Engine
Scenario & Sensitivity Engine — the war gaming brain of finance. Stress-tests business models by shifting volume, price, and cost levers across named scenarios, then runs tornado sensitivity and Monte Carlo simulation to surface what breaks and when. Feeds board decks, risk management, and planning.
Organizational Immune System
Detects and responds to organizational threats - toxic culture patterns, process failures, compliance drift - using an immune system metaphor. Classifies threats, measures organizational antibody strength, and recommends targeted responses before problems become systemic.
Policy Collision Detector
Scans organizational policies for hidden conflicts, contradictions, and coverage gaps. Performs pairwise rule comparison, traces conflicting outcomes through employee scenarios, and scores collision severity to prevent policy chaos before it creates compliance risk.
Decision Fatigue Index
Measures manager decision fatigue by analyzing decision volume, complexity, reversal rates, and quality trends over time. Uses anomaly detection to flag managers approaching cognitive overload and recommends delegation strategies before decision quality degrades.
Autonomous Decision Agent
Evaluates which organizational decisions can safely be delegated to autonomous systems. Scores decision types across reversibility, impact magnitude, data sufficiency, and regulatory constraints to build a graduation roadmap from human-in-the-loop to full autonomy.
How It Works
Three steps to structured, auditable decisions.
Design Agent Workflows
Use Agent Studio to define multi-agent pipelines. Set system prompts, tool access, model selection, and governance rules for each agent.
Deploy with Shadow Mode
Test agents in shadow mode - they run alongside production but don't execute actions. Review outputs before going live.
Govern & Track Outcomes
Agents pass through governance gates on every action. Record outcomes, calibrate accuracy, and feed results back into future runs.
Your AI agents fire and forget. No governance gates, no decision context, no outcome tracking. Every agent action is a compliance risk waiting to happen.
LangChain / CrewAI
Agent frameworks with no governance, audit trail, or decision context
Custom orchestration scripts
Fragile pipelines that break when agents change and produce no audit evidence
Manual agent supervision
Humans reviewing every agent output - defeats the purpose of automation
Standalone agent monitoring
Observability without governance - you can see what agents do, but can't stop bad actions