Product / KynticAI Agentic Importance Framework
KynticAI Agentic Importance Framework
Agentic workflows powered by evidence-weighted importance scoring.
Gather evidence, score what matters, ask better questions, compress context, route work, and generate safe dossiers.
Agent run / Framework
The Agentic sale is seeing a private agent pause, score, ask, and then act.
The buyer should feel the difference immediately: this is not an agent that grabs more context and writes more text. It ranks evidence, asks for the missing variable, and gives the team a reviewable dossier.
1. Agent receives the job
request = Build a renewal-risk dossier for Northstar Account
sources = CRM, support, usage summary, renewal calendar
target = next action for customer success
2. Framework scores the evidence
high_signal = open severity-two ticket
high_signal = usage down over the current review window
medium_signal = sponsor asked for security proof
deferred = old campaign engagement
3. Agent asks before it guesses
missing = latest engineering update
contradiction = usage down but executive sponsor still active
question = confirm owner and recovery status before final action
4. Dossier leaves the runtime
next_action = book technical recovery call
owner = customer success lead
model_route = local or approved adapter under deployment policy
review = human approval before customer message
Data in, data out
Agentic Importance turns evidence into a private decision-support workflow.
The buyer does not need to understand the engine first. Evidence enters, KynticAI scores what matters, the agent asks for missing data, and the team gets a safe next action or dossier.
Workflow evidence enters
CRM notes, support cases, policies, account signals, applications, supplier updates, tickets, or diligence material enter under the approved scope.
The agent learns what matters
Importance scoring ranks the evidence, separates noise, and keeps contradictions or missing data visible.
The agent asks better questions
If a decision would be brittle, the workflow asks for the missing owner, latest source, review note, or contradiction check.
A safe dossier comes out
The output is a next action, explanation, missing-data note, or provenance-aware report routed through the approved model path.
Example input
Customer renewal agent
support ticket = unresolved API issue
usage trend = down
renewal date = next 45 days
CRM note = sponsor wants technical confidence
Example output
importance = elevated because recent support and renewal timing align
missing_data = latest engineering update
question = confirm owner and recovery status
dossier = next action with evidence and caveat notes
Buyer result
The buyer sees a private agent behave like a disciplined analyst: score first, ask when needed, then produce a reviewable next action.
Problem
Agents collect more context and spend more tokens without knowing what matters.
Private agents can become expensive collectors: more files, more notes, more retrieval, more model calls, and still no disciplined view of which evidence deserves attention.
Solution
Turn the Importance Engine into an agentic workflow layer.
KynticAI Agentic Importance Framework scores what matters, explains why, asks for missing evidence, compresses context, routes work, reports safely, and monitors caveats with provenance and review ownership attached.
Data it creates
The agent produces inspectable decision-support artefacts.
These public-safe examples show the shape of the records the framework can create: selected evidence, missing-data notes, contradiction markers, compact context, and safe dossier output. They do not expose private raw weights or scoring tables.
Scored evidence plan
workflow = churn_review
selected_evidence = support_case + usage_change + sponsor_note
importance_band = elevated
reason_category = active_risk + recent_change
deferred_evidence = old_campaign_notes
The agent starts from a ranked evidence plan instead of a large undifferentiated context pack.
Question and contradiction note
missing_data = renewal_owner
contradiction = usage_up + complaint_open
ask = confirm renewal owner and latest support sentiment
review_need = account_owner
caveat = telemetry window incomplete
The agent asks a better question before producing a brittle recommendation.
Safe dossier output
output_type = next_action_dossier
recommendation = schedule technical recovery call
evidence = support_case_42 + usage_14d + sponsor_note
runtime = loopback_ollama
scoring_boundary = protected
The team gets a next action with evidence, caveats, and review ownership.
Buyer spark
The useful agent is the one that knows what not to spend tokens on.
The framework gives local-first teams a way to build decision-support agents that gather evidence, score it, ask sharper questions, and produce safe dossiers instead of long ungrounded answers.
Evidence is ranked before generation.
Missing data and contradictions are raised before the answer hardens.
Runtime routing can use local Ollama, internal gateways, OpenAI-compatible adapters, or Anthropic-compatible adapters under deployment policy.
Workflow
From gathered evidence to safe dossier.
Gather evidence.
Collect approved facts, summaries, signals, and prior outcomes inside the agreed customer boundary.
Score importance.
Rank what matters for the outcome before the agent spends model time or generates a dossier.
Identify missing data and contradictions.
Raise gaps, conflicting evidence, and review needs before the workflow produces a recommendation.
Build compact model context.
Create a smaller, provenance-aware context plan from high-signal evidence and deferred lower-signal material.
Route to local, internal, OpenAI-compatible, or Anthropic-compatible adapters.
Use the deployment's approved model route: local loopback Ollama, an internal model gateway, or governed OpenAI-compatible and Anthropic-compatible provider adapters.
Generate safe answer, next action, or dossier.
Produce a next step, explanation, missing-data note, forensic dossier, or provenance-aware report with evidence, caveats, and review ownership attached.
Example scenarios
Where agentic importance changes the work.
Sales deal review
Before
A rep asks an agent to summarise a deal. The agent retrieves CRM notes, call summaries, usage snippets, and product pages, then generates a long answer without knowing which signal should move the deal.
With KynticAI
The framework scores commercial intent, objection history, sponsor strength, recent product use, and missing procurement data before routing compact context to local Ollama.
The output is a next-action dossier: who to contact, which evidence supports the move, what is missing, and which caveat needs review.
Credit or hiring review
Before
A team asks for a recommendation and receives fluent prose that blends strong evidence, stale notes, contradictions, and missing source checks.
With KynticAI
The framework separates evidence by relevance, flags contradictions, asks for the missing variable, and keeps the final output inside decision-support boundaries.
The team receives a provenance-aware report with missing-data notes, confidence context, and a clear review route.
Supply-chain incident dossier
Before
Operations teams gather supplier updates, tickets, shipment events, and warehouse notes manually, then spend model tokens on repeated summaries.
With KynticAI
Importance scoring ranks the incident evidence, compresses medium-signal updates, defers noise, and prepares a safe local context plan.
The dossier shows the likely next action, evidence trail, contradictions, and the owner who should review the decision.
Runtime
Route through local or approved provider adapters.
KynticAI Agentic Importance Framework can route compact, scored context through local loopback Ollama, internal model gateways, OpenAI-compatible gateways, and Anthropic-compatible gateways where the deployment has approved that route.
Local loopback Ollama
Use local model execution for private agent proof and customer-controlled runtime paths.
OpenAI-compatible gateway
Route through an approved OpenAI-compatible adapter when the deployment policy allows hosted model use.
Anthropic-compatible gateway
Route through an approved Anthropic-compatible adapter when the deployment policy allows hosted model use.
Internal model gateway
Connect scored context to private model routers, internal LLMs, or customer-approved AI platforms.
Output
Safe outputs for decision-support teams.
Next actions
Prioritised actions for a sales, churn, credit, hiring, operations, supply-chain, diligence, or support workflow.
Explanations
Plain-English reasoning with evidence references, caveats, and review prompts.
Forensic dossiers
Evidence-cited findings for claims, contradictions, recurrence, and missing proof.
Missing-data notes
The variables, source gaps, or contradictions the agent should ask about before going further.
Provenance-aware reports
Reports that keep source trails and caveats attached instead of flattening evidence into unsupported prose.
Trust boundary
Keep scoring IP and conversation data inside the right boundary.
The framework gives buyers a protected scoring boundary, customer-controlled runtime choices, and governed provider-adapter routes for sensitive agentic workflows.
Protected scoring weights
Outputs use reason categories, importance bands, provenance, and caveats while private scoring values stay inside the scoring boundary.
Private scoring tables stay protected
Generated dossiers receive the context plan and explanation bands, while proprietary scoring tables remain inside the protected runtime.
Customer-controlled conversation data
Deployment policy decides whether work routes locally, through an internal gateway, or through an approved hosted-provider adapter.
Approved deployment adapters
Local Ollama, internal model gateways, OpenAI-compatible gateways, and Anthropic-compatible gateways are described as governed deployment routes.
Agentic deployment
Build a local-first agent that scores evidence before it answers.
Scope a private decision-support agent for sales, churn, credit, hiring, operations, supply chain, diligence, or support with the model route your deployment approves.