Decision Intelligence

AgentContext.record_decision() stores every AI decision as a node in the knowledge graph, linked by causal edges to the decisions that preceded it and the outcomes that followed. Use it to build an auditable reasoning trail — one that lets you reconstruct, six months later, exactly which classification caused which escalation, and which policy was checked before it was recorded.

What Is Decision Intelligence?

Decision Intelligence records and analyzes an agent’s own decisions as structured data that can be queried, analyzed, and reused. Instead of decisions disappearing after execution, they become persistent graph nodes with searchable metadata, reasoning chains, and causal relationships. Decision Intelligence records decisions by capturing the scenario, reasoning, outcome, confidence, and decision maker for each choice the agent makes. These decisions become queryable nodes in your knowledge graph. Decisions become graph nodes that can be linked causally (Decision A caused Decision B), searched by similarity (find decisions like this scenario), and analyzed statistically (confidence trends, common outcomes). The goal is auditability, explainability, precedent search, and causal tracing. You can trace why decisions were made, find similar past decisions for consistency, and understand the full causal chain from initial detection to final action. Decision Intelligence vs. Agent Memory: Agent Memory stores external knowledge (documents, facts, observations). Decision Intelligence stores internal decisions (classifications, approvals, actions the agent itself made). Decision Intelligence vs. Reasoning: Reasoning derives new facts from existing data using logical rules. Decision Intelligence records the choices and judgments the agent made during problem-solving. Decision Intelligence vs. Graph Analytics: Graph Analytics analyzes the structural properties of your knowledge graph. Decision Intelligence focuses specifically on the decision-making process and its audit trail.

Why Use Decision Intelligence?

Auditable AI actions. Every decision is recorded with reasoning, confidence, and timestamp, creating a complete audit trail for AI behavior in production systems. Explainability. When stakeholders ask “why did the system do X?”, you can trace the exact decision chain that led to that action, including intermediate reasoning steps. Precedent reuse. Before making new decisions, agents can search for similar past scenarios and their outcomes, promoting consistency and learning from previous experience. Causal analysis. Understand how early decisions cascade into later outcomes by following causal relationships between linked decision nodes. Governance and compliance. Policy engines can gate decisions against compliance rules, and all policy applications are recorded for regulatory audit.

When To Use / When Not To Use

Use Decision Intelligence when:

Building autonomous agents that make consequential choices
Implementing decision workflows requiring audit trails
Operating under compliance requirements (financial services, healthcare, defense)
Building approval systems with multiple decision points
Working in risk-sensitive environments where decisions must be explainable

Do not use when:

Building stateless chatbots that only retrieve information
Implementing simple RAG systems without decision-making
Creating read-only information retrieval applications
Building applications that never make actionable decisions requiring audit trails

API Architecture Overview

Decision Intelligence coordinates three main components: AgentContext serves as the high-level orchestration layer. It provides record_decision(), find_precedents(), and causal chain methods while managing the underlying storage and retrieval systems. PolicyEngine handles policy evaluation and compliance checking. It stores policy rules as graph nodes and validates decisions against those rules before they’re recorded. DecisionRecorder specializes in recording structured decision data, managing approval chains, and handling policy exceptions when decisions need to bypass normal rules.

Decision tracking requires both a VectorStore (for embedding-based precedent search) and a ContextGraph (for causal graph storage). Set decision_tracking=True on AgentContext — omitting ContextGraph raises a RuntimeError at call time. VectorStore is required by AgentContext itself: leaving the argument out raises a TypeError from Python’s argument binding, while passing vector_store=None raises a ValueError during initialization.

Recording the First Decision

The most common entry point is AgentContext.record_decision(). It writes a Decision node into the graph, generates embeddings for hybrid similarity search, and returns a UUID you use to link subsequent decisions causally.

from semantica.context import AgentContext, ContextGraph
from semantica.vector_store import VectorStore

graph   = ContextGraph(advanced_analytics=True)
context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=graph,
    decision_tracking=True,
)

# The system has just classified a new threat cluster.
# Record the classification decision before taking any action.
classification_id = context.record_decision(
    category       = "threat_classification",
    scenario       = "Unattributed C2 cluster using HAMMERTOSS-like Twitter dead-drop pattern",
    reasoning      = "Infrastructure overlaps known APT29 hosting ASN; TTP T1102 matches NOBELIUM playbook with 0.88 cosine similarity",
    outcome        = "classified_as_apt29_cluster",
    confidence     = 0.88,
    decision_maker = "cti_pipeline_v2",
    entities       = ["apt29", "hammertoss", "twitter_c2"],
)

print("Decision recorded:", classification_id)
# → "Decision recorded: dec_a3f2b1c4-..."

The decision_maker field identifies the component, workflow, agent, or system that produced this decision. Use consistent identifiers like "cti_pipeline_v2", "analyst_chen", or "risk_model_v3" to enable filtering and analysis by decision source. The Decision dataclass that backs this node has the following fields — these are what get stored and searched:

from semantica.context import Decision
from datetime import datetime

# Constructing a Decision explicitly (alternative to record_decision)
d = Decision(
    decision_id    = "dec_001",           # UUID — auto-generated if omitted via record_decision
    category       = "threat_classification",
    scenario       = "Unattributed C2 cluster",
    reasoning      = "Infrastructure overlaps APT29 ASN",
    outcome        = "classified_as_apt29_cluster",
    confidence     = 0.88,               # float 0.0–1.0
    timestamp      = datetime.now(),
    decision_maker = "cti_pipeline_v2",
    # optional fields:
    valid_from     = "2025-07-01T00:00:00",   # ISO datetime
    valid_until    = "2025-09-30T23:59:59",   # ISO datetime
    metadata       = {"source_feed": "isac_partner_b"},
)
graph.add_decision(d)

Searching Precedents Before Deciding

Before making a significant call, the system should search past decisions for similar scenarios. This is how you prevent the same cluster being classified differently across two agent runs — the second agent finds the first agent’s decision and uses it as a prior.

# Search before classifying a new unattributed cluster
precedents = context.find_precedents(
    "unattributed C2 cluster Twitter dead-drop infrastructure",
    limit=5,
)

for p in precedents:
    print("[{:.2f} confidence] {} → {}".format(p.confidence, p.category, p.outcome))
    print("  Reasoning: {}".format(p.reasoning[:80]))
    print("  Similarity: {:.3f}".format(p.metadata.get("similarity_score", 0)))

Hybrid search blends two signals: semantic similarity over the scenario and reasoning text (weight 0.7), and structural graph proximity via Node2Vec embeddings (weight 0.3). The result is a ranked list of Decision objects — the most similar past decisions float to the top regardless of how differently they were phrased.

Building a Causal Chain

Decisions rarely exist in isolation. A classification decision causes an escalation decision, which causes a containment decision. Linking them with causal edges lets you traverse the chain in either direction — upstream to understand what caused an outcome, downstream to see what an early decision triggered.

# The classification above caused an escalation
escalation_id = context.record_decision(
    category       = "escalation",
    scenario       = "APT29 cluster confirmed — active C2 beaconing to NATO contractor subnet",
    reasoning      = "Classification confidence 0.88 exceeds 0.80 escalation threshold; active C2 requires immediate SOC notification",
    outcome        = "escalated_to_soc_tier2",
    confidence     = 0.95,
    decision_maker = "escalation_engine",
)

# Link the two decisions: classification caused the escalation
graph.add_causal_relationship(classification_id, escalation_id, "CAUSED")

# The escalation influenced a patch prioritization decision
patch_id = context.record_decision(
    category       = "patch_priority",
    scenario       = "CVE-2024-3400 present in two NATO contractor VPN appliances",
    reasoning      = "Active exploitation by classified APT29 cluster elevates CVE-2024-3400 to P0 regardless of base CVSS",
    outcome        = "prioritized_cve_2024_3400_p0",
    confidence     = 0.97,
    decision_maker = "patch_engine",
)

graph.add_causal_relationship(escalation_id, patch_id, "INFLUENCED")

Now trace the chain from the patch decision back to its root cause:

upstream = context.get_causal_chain(
    patch_id,
    direction = "upstream",
    max_depth = 5,
)

print("Causal chain upstream from patch prioritization:")
for d in upstream:
    depth = d.metadata.get("causal_distance", "?")
    print("  [depth {}] {} → {}  (confidence={:.2f})".format(
        depth, d.category, d.outcome, d.confidence
    ))
# [depth 1] escalation → escalated_to_soc_tier2  (confidence=0.95)
# [depth 2] threat_classification → classified_as_apt29_cluster  (confidence=0.88)

And trace downstream from the original classification to see everything it triggered:

downstream = context.get_causal_chain(
    classification_id,
    direction = "downstream",
    max_depth = 5,
)
print("Downstream decisions triggered:", len(downstream))
for d in downstream:
    print("  → {} [{}]".format(d.outcome, d.category))

Generating an Explainability Report

trace_decision_explainability gives you the full picture in one call: upstream causes, downstream effects, and total connection count. This is what you attach to a post-mortem or audit report.

explanation = context.trace_decision_explainability(patch_id)

print("Decision:", patch_id)
print("Total graph connections :", explanation["total_connections"])
print("Upstream causes         :", len(explanation.get("upstream_decisions", [])))
print("Downstream effects      :", len(explanation.get("downstream_decisions", [])))

For deeper causal analysis with confidence decay and distance bands, use trace_decision_causality on the graph directly:

chains = graph.trace_decision_causality(patch_id, max_depth=5)

for chain in chains:
    print("Chain: {} hops | band={} | decay={:.3f}".format(
        chain["hop_count"], chain["distance_band"], chain["confidence_decay"]
    ))
    print("  Interpretation:", chain["interpretation"])
    # e.g. "Decision chain spans 2 hops in the 'near' band with 84% confidence
    #        — causal attribution is reliable."

Gating Decisions Against Policy

Before recording a high-stakes decision, check it against a versioned policy. The PolicyEngine stores Policy nodes in the graph and gates Decision objects against their rules.

from semantica.context import PolicyEngine, Policy, Decision
from datetime import datetime

engine = PolicyEngine(graph_store=graph)

engine.add_policy(Policy(
    policy_id   = "cti_confidence_gate",
    name        = "CTI Minimum Confidence Policy",
    description = "All threat classifications must have confidence >= 0.80",
    rules       = {"min_confidence": 0.80, "requires_reasoning": True},
    category    = "threat_classification",
    version     = "1.0",
    created_at  = datetime.now(),
    updated_at  = datetime.now(),
))

d = Decision(
    decision_id    = "dec_low_conf",
    category       = "threat_classification",
    scenario       = "Possible APT29 activity — weak signals only",
    reasoning      = "Single IP overlap, no TTP match",
    outcome        = "classified_as_apt29_tentative",
    confidence     = 0.62,   # below the 0.80 threshold
    timestamp      = datetime.now(),
    decision_maker = "cti_pipeline_v2",
)

if engine.check_compliance(d, "cti_confidence_gate"):
    graph.add_decision(d)
    engine.record_policy_application(d.decision_id, "cti_confidence_gate", "1.0")
    print("Decision recorded — policy compliant.")
else:
    print("Decision blocked — confidence 0.62 below policy minimum 0.80.")
    # → "Decision blocked — confidence 0.62 below policy minimum 0.80."

When a high-urgency situation requires bypassing the policy gate, record the exception with the approver identity and justification:

from semantica.context import DecisionRecorder

recorder = DecisionRecorder(graph_store=graph)

exception_id = recorder.record_exception(
    decision_id     = "dec_low_conf",
    policy_id       = "cti_confidence_gate",
    reason          = "Active exploitation in progress — cannot wait for higher-confidence attribution",
    approver        = "ciso_director",
    approval_method = "slack_dm",
    justification   = "Time-critical incident response; manual CISO sign-off obtained at 03:14 UTC",
)
print("Policy exception recorded:", exception_id)

For multi-level approval workflows, use DecisionRecorder.record_approval_chain() with a graph database backend (for example Neo4j/FalkorDB). The in-memory ContextGraph examples used in this guide do not support approval-chain persistence via execute_query().

Generating a Decision Audit Report

At the end of a shift or incident, get_decision_insights produces a statistical summary of every decision in the graph — useful for shift handover notes and compliance reporting.

insights = graph.get_decision_insights()

print("Total decisions today :", insights["total_decisions"])
print("Confidence — mean={:.2f}  min={:.2f}  max={:.2f}".format(
    insights["confidence_stats"]["mean"],
    insights["confidence_stats"]["min"],
    insights["confidence_stats"]["max"],
))
print("\nDecisions by category:")
for cat, count in sorted(insights["categories"].items(), key=lambda x: -x[1]):
    print("  {:35s} {}".format(cat, count))
print("\nOutcomes:")
for outcome, count in sorted(insights["outcomes"].items(), key=lambda x: -x[1]):
    print("  {:35s} {}".format(outcome, count))

Sample output:

Total decisions today : 47
Confidence — mean=0.87  min=0.62  max=0.99

Decisions by category:
  threat_classification                 18
  patch_priority                        12
  escalation                             9
  containment                            8

Outcomes:
  classified_as_apt29_cluster           11
  prioritized_p0_patch                  12
  escalated_to_soc_tier2                 9
  isolated_host                          8

Domain Examples

Defense — CTI/Threat
Security — SOC/Incident
Life Science — Clinical/Pharma
Banking — Risk/Compliance

A CTI pipeline classifies threat clusters, records each classification with confidence and reasoning, links classification decisions to escalation decisions causally, and generates a daily audit report for the threat intelligence lead.

from semantica.context import AgentContext, ContextGraph, PolicyEngine, Policy
from semantica.vector_store import VectorStore
from datetime import datetime

graph   = ContextGraph(advanced_analytics=True)
engine  = PolicyEngine(graph_store=graph)
context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=graph,
    decision_tracking=True,
)

engine.add_policy(Policy(
    policy_id   = "cti_gate",
    name        = "CTI Attribution Confidence Gate",
    description = "Attributions require confidence >= 0.80",
    rules       = {"min_confidence": 0.80},
    category    = "threat_classification",
    version     = "1.0",
    created_at  = datetime.now(),
    updated_at  = datetime.now(),
))

# Check precedents before classifying
precedents = context.find_precedents(
    "Twitter dead-drop C2 pattern overlapping APT29 infrastructure",
    limit=3,
)
for p in precedents:
    print("Prior: {} → {} ({:.0%})".format(p.scenario[:40], p.outcome, p.confidence))

# Record the classification
class_id = context.record_decision(
    category       = "threat_classification",
    scenario       = "New C2 cluster: Twitter dead-drop, AS200651 hosting, TTP T1102",
    reasoning      = "IP block overlaps APT29 cluster; T1102 matches HAMMERTOSS playbook",
    outcome        = "classified_apt29_march_cluster",
    confidence     = 0.88,
    decision_maker = "cti_pipeline_v2",
    entities       = ["apt29", "hammertoss"],
)

# Link to downstream escalation
esc_id = context.record_decision(
    category       = "escalation",
    scenario       = "APT29 cluster active — beaconing to NATO subnet 10.30.0.0/16",
    reasoning      = "Active C2 with high-confidence attribution requires immediate SOC notification",
    outcome        = "escalated_tier2_soc",
    confidence     = 0.97,
    decision_maker = "escalation_engine",
)
graph.add_causal_relationship(class_id, esc_id, "CAUSED")

# Post-shift audit report
insights = graph.get_decision_insights()
print("Decisions recorded:", insights["total_decisions"])
print("Mean confidence   :", round(insights["confidence_stats"]["mean"], 2))

During an incident, the SOC records containment decisions with causal links to the detection decisions that triggered them. Six hours later the post-mortem can replay the exact decision sequence from first alert to final containment.

from semantica.context import AgentContext, ContextGraph, DecisionRecorder
from semantica.vector_store import VectorStore

graph    = ContextGraph()
recorder = DecisionRecorder(graph_store=graph)
context  = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=graph,
    decision_tracking=True,
)

# T+0: Detection decision
detect_id = context.record_decision(
    category       = "detection",
    scenario       = "WKSTN-047: wmiprvse.exe spawned scheduled task — T1053.005",
    reasoning      = "Scheduled task creation by WMI provider host is high-fidelity lateral movement indicator",
    outcome        = "flagged_wkstn047_suspicious",
    confidence     = 0.93,
    decision_maker = "edr_engine",
)

# T+8min: Containment decision caused by detection
contain_id = context.record_decision(
    category       = "containment",
    scenario       = "WKSTN-047 confirmed compromised — lateral movement to DC01 via SMB",
    reasoning      = "PsExec artefact on DC01; isolate before domain-wide credential compromise",
    outcome        = "isolated_wkstn047",
    confidence     = 0.95,
    decision_maker = "analyst_chen",
)
graph.add_causal_relationship(detect_id, contain_id, "CAUSED")

# Post-mortem: trace the full chain
chain = context.get_causal_chain(contain_id, direction="upstream", max_depth=5)
print("Post-mortem — causal chain for isolation decision:")
for d in chain:
    print("  [depth {}] {} → {}  (confidence={:.2f}, maker={})".format(
        d.metadata.get("causal_distance", "?"),
        d.category, d.outcome, d.confidence, d.decision_maker,
    ))

# Full explainability for the containment decision
explanation = context.trace_decision_explainability(contain_id)
print("Upstream causes   :", len(explanation.get("upstream_decisions", [])))
print("Total connections :", explanation["total_connections"])

A clinical AI assistant records treatment modification decisions with guideline precedents, links them causally to the diagnostic decisions that preceded them, and generates a structured decision record for MDT review and regulatory audit.

from semantica.context import AgentContext, ContextGraph, PolicyEngine, Policy
from semantica.context import Decision, DecisionRecorder
from semantica.vector_store import VectorStore
from datetime import datetime

graph   = ContextGraph()
engine  = PolicyEngine(graph_store=graph)
context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=graph,
    decision_tracking=True,
)

engine.add_policy(Policy(
    policy_id   = "clinical_confidence_gate",
    name        = "Clinical Decision Confidence Gate",
    description = "Treatment decisions require confidence >= 0.90",
    rules       = {"min_confidence": 0.90},
    category    = "treatment_modification",
    version     = "1.0",
    created_at  = datetime.now(),
    updated_at  = datetime.now(),
))

# Diagnostic decision precedes treatment decision
diag_id = context.record_decision(
    category       = "diagnosis_assessment",
    scenario       = "Patient eGFR 28 mL/min/1.73m2, CKD Stage 4, current metformin 1000mg BD",
    reasoning      = "eGFR 28 confirms CKD Stage 4; below 30 threshold for metformin contraindication",
    outcome        = "confirmed_ckd_stage4_metformin_contraindicated",
    confidence     = 0.99,
    decision_maker = "clinical_ai_v3",
)

# Treatment modification caused by diagnostic assessment
treat_id = context.record_decision(
    category       = "treatment_modification",
    scenario       = "Metformin discontinuation required — eGFR 28 below contraindication threshold",
    reasoning      = "NICE NG28 and BNF both contraindicate metformin at eGFR < 30; switch to gliclazide MR 30mg OD",
    outcome        = "discontinue_metformin_initiate_gliclazide",
    confidence     = 0.97,
    decision_maker = "clinical_ai_v3",
)
graph.add_causal_relationship(diag_id, treat_id, "CAUSED")

# MDT audit report
chain = context.get_causal_chain(treat_id, direction="upstream", max_depth=3)
print("MDT Decision Audit — Treatment Modification")
print("=" * 50)
for d in chain:
    print("Caused by: [{}] {} (confidence={:.0%})".format(
        d.category, d.outcome, d.confidence
    ))

insights = graph.get_decision_insights()
cs = insights["confidence_stats"]
print("\nSession decisions: {}  |  mean confidence: {:.2f}".format(
    insights["total_decisions"], cs["mean"]
))

A credit decisioning system records every loan decision against a versioned lending policy, links borderline approvals to stress-test decisions causally, and exports a complete decision audit trail for SR 11-7 model governance review.

from semantica.context import AgentContext, ContextGraph, PolicyEngine, Policy
from semantica.context import Decision, DecisionRecorder
from semantica.vector_store import VectorStore
from datetime import datetime

graph   = ContextGraph()
engine  = PolicyEngine(graph_store=graph)
context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=graph,
    decision_tracking=True,
)

engine.add_policy(Policy(
    policy_id   = "lending_policy_v3",
    name        = "Lending Compliance Policy v3",
    description = "Credit decisions require confidence >= 0.85 and documented reasoning",
    rules       = {"min_confidence": 0.85, "requires_reasoning": True},
    category    = "loan_approval",
    version     = "3.0",
    created_at  = datetime.now(),
    updated_at  = datetime.now(),
))

# Check precedents before approving
precedents = context.find_precedents(
    "first-time buyer mortgage borderline DSTI stressed rate scenario",
    limit=3,
)
for p in precedents:
    print("Prior: {} → {} ({:.0%})".format(p.scenario[:40], p.outcome, p.confidence))

# Stress-test decision precedes approval decision
stress_id = context.record_decision(
    category       = "stress_test",
    scenario       = "APP-2025-994421: LTV 78%, DSTI 38% at current rate; DSTI rises to 44% at +300bps",
    reasoning      = "DSTI 44% under stress exceeds 35% guideline threshold — requires LMI and income verification",
    outcome        = "stress_test_conditional_pass",
    confidence     = 0.88,
    decision_maker = "risk_model_v3",
)

# Approval decision influenced by stress test
d = Decision(
    decision_id    = "loan_dec_994421",
    category       = "loan_approval",
    scenario       = "APP-2025-994421: first-time buyer, LTV 78%, credit score 714, 30yr fixed",
    reasoning      = "Credit score 714 exceeds 700 minimum; LTV within 80% cap; conditional on LMI given stress-test DSTI",
    outcome        = "approved_conditional_lmi_required",
    confidence     = 0.89,
    timestamp      = datetime.now(),
    decision_maker = "credit_model_v3",
)

if engine.check_compliance(d, "lending_policy_v3"):
    loan_id = context.record_decision(
        category=d.category, scenario=d.scenario,
        reasoning=d.reasoning, outcome=d.outcome, confidence=d.confidence,
        decision_maker=d.decision_maker,
    )
    graph.add_causal_relationship(stress_id, loan_id, "INFLUENCED")
    engine.record_policy_application(d.decision_id, "lending_policy_v3", "3.0")
    print("Loan decision recorded — policy compliant.")

    # SR 11-7 explainability report
    explanation = context.trace_decision_explainability(loan_id)
    print("Upstream influences:", len(explanation.get("upstream_decisions", [])))
    print("Total connections  :", explanation["total_connections"])

Persisting Decisions Across Restarts

When using the local ContextGraph, save at the end of every session and load at the start of the next. All decision nodes, causal edges, and FAISS embeddings are restored.

# End of session
context.save("agent_state/")
# Writes: agent_state/knowledge_graph.json + FAISS index

# Start of next session
context = AgentContext(
    vector_store=VectorStore(backend="faiss", dimension=768),
    knowledge_graph=ContextGraph(),
    decision_tracking=True,
)
context.load("agent_state/")

# All past decisions are searchable immediately
results = context.find_precedents("APT29 infrastructure attribution", limit=5)

Common Pitfalls

Recording decisions without linking causal relationships. Isolated decision nodes provide less insight than connected decision chains. Use add_causal_relationship() to link related decisions and enable causal tracing. Creating isolated decision nodes. Decisions gain value when connected to entities, other decisions, or outcomes in your graph. Link decisions to relevant entities using the entities parameter. Recording too many low-value decisions. Not every minor choice needs permanent recording. Focus on consequential decisions that affect outcomes, require audit trails, or benefit from precedent search. Treating precedent similarity as proof. High similarity scores indicate related scenarios, not identical situations. Use precedents as guidance while considering the specific context of each new decision. Using Decision Intelligence when simple retrieval is sufficient. If your system only retrieves information without making actionable choices, traditional search or Agent Memory may be more appropriate than decision tracking.

Context Graphs — how ContextGraph stores decision nodes and causal edges
Distance Intelligence — trace_decision_causality() annotates causal chains with confidence decay and distance bands
Provenance — W3C PROV-O audit trail that wraps decision records in standards-compliant provenance
MCP Server — expose decision recording and precedent search to LLM agents via the record_decision and find_precedents tools
Change Management — checkpoint decision state with flush_checkpoint() for versioned snapshots

Overview

Core Concepts

Guides

Integrations

Graph Stores

Vector Stores

Decision Intelligence

What Is Decision Intelligence?

Why Use Decision Intelligence?

When To Use / When Not To Use

API Architecture Overview

Recording the First Decision

Searching Precedents Before Deciding

Building a Causal Chain

Generating an Explainability Report

Gating Decisions Against Policy

Generating a Decision Audit Report

Domain Examples

Persisting Decisions Across Restarts

Common Pitfalls

​What Is Decision Intelligence?

​Why Use Decision Intelligence?

​When To Use / When Not To Use

​API Architecture Overview

​Recording the First Decision

​Searching Precedents Before Deciding

​Building a Causal Chain

​Generating an Explainability Report

​Gating Decisions Against Policy

​Generating a Decision Audit Report

​Domain Examples

​Persisting Decisions Across Restarts

​Common Pitfalls

​Related Guides

What Is Decision Intelligence?

Why Use Decision Intelligence?

When To Use / When Not To Use

API Architecture Overview

Recording the First Decision

Searching Precedents Before Deciding

Building a Causal Chain

Generating an Explainability Report

Gating Decisions Against Policy

Generating a Decision Audit Report

Domain Examples

Persisting Decisions Across Restarts

Common Pitfalls

Related Guides