Anitech AI-Native SDLC / Canonical Operating Workflow
The detailed operating process for an AI-native Anitech workflow. Work starts as a signal, becomes a bounded bet, is run by a temporary human-agent workcell, proves value and risk control through live evidence, then ships, stops, scales, or becomes a repeatable operating process.
BetFlow OS replaces ceremony-led delivery with decision-led flow. The unit of control is not a sprint or a ticket. The unit of control is a bet with an owner, appetite, risk tier, workcell, proof standard, and next allocation decision.
This loop is the simple mental model. The detailed process map below expands every touch point, stakeholder, tool, meeting, decision, and output.
Signals arrive from clients, telemetry, sales, support, delivery friction, finance, security, or strategic opportunity.
Leaders decide whether the signal deserves appetite, what risk tier applies, and who owns the outcome.
A temporary workcell forms around the bet with people, agents, context, tools, guardrails, tests, and cadence.
The workcell builds, validates, releases, and maintains live proof of value, quality, safety, and operating fit.
The bet ships, stops, scales, narrows, re-bets, or becomes standard operating practice.
Read left to right. On mobile, scroll horizontally. This is the canonical workflow structure for the BetFlow OS artifact.
Each stage has a clear purpose, owner, decision point, and output. This section is the operating playbook version of the map.
Signals are collected continuously and reviewed quickly so the company can distinguish noise from work worth shaping.
A signal becomes decision-ready before leadership commits appetite to it.
Leadership commits bounded appetite, not an open-ended project promise.
The workcell is a temporary operating unit built around the bet, not a permanent team ritual.
Work progresses through visible proof, not status theatre.
The work ends with an allocation decision and a memory update, not just a release note.
These are accountabilities, not always separate jobs. Smaller bets can combine roles, but every cell still needs an owner.
The cadence is intentionally light. Meetings exist for judgement, commitment, exceptions, and learning. Everything else should be asynchronous or agent-generated.
15 minutes or async digest. Review new signals, reject noise, assign owners, and route material items for shaping.
Commit appetite, name owners, set risk tier, approve proof standard, stop weak bets, and reallocate capacity.
Confirm charter, tools, agent permissions, context pack, proof ledger, review path, and first execution plan.
Resolve blocked decisions, policy uncertainty, client expectation issues, system risk, or proof gaps.
Check evidence against risk tier: outcome, tests, review, security, rollback, telemetry, and client readiness.
Explain what changed, why it matters, evidence observed, limitations, support path, and next decision.
Inspect bottlenecks, agent performance, CI health, quality patterns, delivery flow, and platform friction.
Promote useful learning into playbooks, policies, agent briefs, templates, training, and the roadmap.
Tooling can change, but each capability needs an owner and a consistent source of truth.
Client pain, account risk, conversion signals, support themes, usage anomalies, and escalation indicators.
Signal status, appetite, owner, risk tier, decision date, constraints, dependencies, and stop condition.
Implementation, review, agent tasks, decision records, repo context, workcell flow, and traceability.
Tests, scans, evals, regression evidence, agent output review, provenance, and release confidence.
Context packs, client constraints, architecture decisions, policy, delivery standards, and lessons learned.
Observability, rollback, performance, adoption, incident path, and post-release evidence.
Margin impact, forecast movement, effort burn, revenue impact, client confidence, and renewal signal.
Current bets, decisions needed, proof status, escalations, and routes into detailed artifacts.
The artifacts are deliberately small. They should be generated from work where possible, then approved or corrected by humans.
Source, evidence, stakeholder, urgency, confidence, value hypothesis, risk hints, and suggested owner.
Outcome, appetite, owner, risk tier, assumptions, constraints, proof standard, stop condition, and decision date.
People, agents, tools, permissions, context pack, cadence, escalation path, and evidence plan.
Tests, evals, review notes, screenshots, telemetry, client feedback, risks, decisions, and release readiness.
What shipped, who approved it, evidence observed, limitations, support path, and next client or product decision.
Reusable lesson, policy update, agent prompt or tool change, template improvement, and future bet recommendation.
These rules keep the system fast without allowing AI-assisted work to outrun accountability.
Signals can be watched without an owner, but committed bets must have a named Outcome Owner and decision date.
The Workcell Lead cannot start material work until risk tier and proof standard are explicit.
Agent autonomy is bounded by data access, system access, action type, and review requirement.
Proof is captured during work, not assembled as a decorative pack after the decision is already made.
Status should be generated from systems. Human time is for tradeoffs, approval, exceptions, and learning.
Ship, stop, scale, narrow, re-bet, standardise, or archive. No silent drift back into backlog.
Use this BetFlow OS process map as the current operating artifact. Keep older Agentic BetFlow and client-process pages only as archive references where needed. The dashboard and Anitech gateway should now route people to this page first.