sub-AI (opt) / II. DISTRIBUTED & AUTONOMOUS INTELLIGENCE

Autonomous Execution Systems

Distributed AI services for orchestration, coordination, federated operation, edge intelligence, and monitored execution. Senior AI engineer scope covers goal planner, task decomposition, tool permissions, memory policy, approval gate, and execution trace, using approved business procedures, task examples, tool schemas, exception paths, and human approval rules and measurable checks for task completion, action safety, tool-call accuracy, escalation quality, and recovery from incomplete context.

Architecture & Risk Blueprint

Senior AI engineering discovery for architecture, risk boundaries, data readiness, and implementation scope.

For Autonomous Execution Systems, the AI engineer will map target workflows, data owners, integration points, and known failure modes before design starts
Define goal planner, task decomposition, tool permissions, memory policy, approval gate, and execution trace with clear service boundaries, control points, and engineering assumptions
Prepare the dataset, model, runtime, access, and logging requirements needed for Autonomous Execution Systems
Build the evaluation plan for task completion, action safety, tool-call accuracy, escalation quality, and recovery from incomplete context so acceptance is measurable, not impression-based
Document risks around unsafe action, data leakage, dependency failure, integration drift, unclear accountability, and evidence gaps and turn them into mitigation tasks with named owners
Deliver architecture diagrams, runbooks, test records, release notes, acceptance criteria, and engineering backlog for procurement, technical review, and implementation approval

USD 1.838.151 IDR 31.708.100.000 Request Scope

Controlled Prototype & Evaluation

A limited working system to test behavior, integration boundaries, evaluation criteria, and operational limits.

For Autonomous Execution Systems, the AI engineer will implement a controlled prototype around the highest-risk workflow, not a presentation mockup
Connect approved business procedures, task examples, tool schemas, exception paths, and human approval rules to a limited runtime using test credentials, sandbox data, and strict access separation
Build the first agent loop, planner, tool adapter, action validator, audit log, retry policy, and stop condition handler slice with traceable inputs, outputs, errors, and reviewer notes
Run evaluation cases for task completion, action safety, tool-call accuracy, escalation quality, and recovery from incomplete context and record pass, fail, and uncertain outcomes
Review security, data handling, prompt or policy behavior, and integration limits before production planning
Produce a prototype report with architecture changes, blocked items, engineering estimates, and release criteria

USD 2.661.345 IDR 45.908.200.000 Request Scope

Recommended

Production Engineering

Production implementation with service boundaries, pipelines, evaluation harness, security controls, and handover.

For Autonomous Execution Systems, the AI engineer will build the production service boundary for Autonomous Execution Systems with repository structure and release workflow
Implement agent loop, planner, tool adapter, action validator, audit log, retry policy, and stop condition handler with environment separation, secrets handling, API contracts, and rollback path
Engineer data preparation, model routing, policy checks, test fixtures, and operational telemetry for the service
Create automated evaluation jobs for task completion, action safety, tool-call accuracy, escalation quality, and recovery from incomplete context before every accepted release
Add audit records, access controls, monitoring operational intelligence interfaces, incident notes, and dependency version tracking
Hand over diagrams, runbooks, acceptance records, deployment notes, and backlog items to the owner team

USD 3.855.554 IDR 66.508.300.000 Request Scope

Operations, Reliability & Governance

Operating model for reliability, monitoring, release control, evidence records, and long-term technical governance.

For Autonomous Execution Systems, the AI engineer will set up operating controls for release approval, model or policy change, incident response, and evidence storage
Define service indicators for task completion, action safety, tool-call accuracy, escalation quality, and recovery from incomplete context with alert thresholds, review cadence, and escalation roles
Create monitoring for agent loop, planner, tool adapter, action validator, audit log, retry policy, and stop condition handler including latency, errors, drift, cost, version changes, and abnormal behavior
Run tabletop recovery checks for unsafe action, data leakage, dependency failure, integration drift, unclear accountability, and evidence gaps and document the decision path for urgent rollback or shutdown
Prepare governance records for access review, data retention, evaluation evidence, vendor dependency, and stakeholder reporting
Maintain an engineering roadmap for reliability, cost, security, and capability improvements after launch

USD 5.090.342 IDR 87.808.400.000 Request Scope