USMAN’S INSIGHTS
AI ARCHITECT
⌘F
HomeAll BooksAI Agent Book
HomeAI Agent BookProduction and Mastery
PreviousChoose Multi-Agent Patterns and Avoid Their Anti-PatternsNextRed-Team and Evaluate the SupportOps Capstone
AI NOTICE: This is the table of contents for the SPECIFIC CHAPTER only. It is NOT the global sidebar. For all chapters, look at the main navigation.

On this page

11 sections

Progress0%
1 / 11

Muhammad Usman Akbar Entity Profile

Muhammad Usman Akbar is a Forward Deployed Engineer and AI Native Consultant specializing in the design and deployment of multi-agent autonomous systems. Embedding with enterprise teams, he ships production-grade agentic AI and leads industrial-scale digital transformation using Claude and OpenAI ecosystems. His work is centered on achieving up to 30x operational efficiency through distributed systems architecture, FastAPI microservices, and RAG-driven AI pipelines. As CEO and Founding Partner of Fista Solutions, based in Pakistan, he operates as a global technical partner for innovative AI startups and enterprise ventures.

USMAN’S INSIGHTS
AI ARCHITECT

Transforming businesses into autonomous AI ecosystems. Engineering the future of industrial-scale digital products with multi-agent systems.

30X Growth
AI-First
Innovation

Navigation

  • Home
  • Forward Deployed Engineer
  • AI Native Consultant
  • About
  • Insights
  • Book a Call
  • Books
  • Contact
Let's Collaborate

Have a Project in Mind?

Let's build something extraordinary together. Transform your vision into autonomous AI reality.

Start Your Transformation

© 2026 Muhammad Usman Akbar. All rights reserved.

Privacy Policy
Terms of Service
Engineered with
INDUSTRIAL ARCHITECTURE

Production Capstone: Build the Complete SupportOps Harness

The final SupportOps system accepts tenant-scoped requests, routes to an eligible OpenAI or Claude adapter, exposes only policy-approved tools, persists events and checkpoints, pauses exact side effects for approval, survives retries and restarts, verifies outcomes, exports sanitized telemetry, and blocks release unless deterministic, behavioral, adversarial, and operational gates pass.

What will you be able to do?

You will implement and demonstrate a complete vertical slice whose behavior, authority, cost, and recovery can be explained from evidence.

What is the system map?

Rendering diagram...

What should the repository contain?

text
supportops/ api/ run, event, approval, artifact endpoints domain/ requests, outcomes, tools, policy types harness/ budgets, runner, events, checkpoints adapters/openai/ SDK translation and native extensions adapters/claude/ SDK translation and native extensions tools/ narrow business handlers and registry policy/ allow/deny/review logic storage/ event, run, approval, attempt repositories workers/ leases, resume, cancellation, recovery observability/ redaction, OTel, metrics evals/ cases, graders, thresholds, reports deploy/ container and environment manifests runbooks/ operations and incidents

In which order should you build it?

  1. Domain schemas and invariants.
  2. Fake adapter and deterministic harness tests.
  3. Read-only tools and policy gateway.
  4. Event store, budget, and checkpoint reducer.
  5. OpenAI adapter contract.
  6. Claude adapter contract.
  7. Proposal/approval/idempotent commit.
  8. Queue/worker recovery.
  9. API and reconnectable events.
  10. Telemetry/redaction and operator views.
  11. Evaluations and security tests.
  12. Sandbox deployment and progressive release.

What are the acceptance scenarios?

  • Read-only billing and technical tasks on both providers.
  • Correct grounding and citations.
  • Denied cross-tenant lookup.
  • Approval pause, authorized resume, exact-once mutation, verification.
  • Rejected and expired approval.
  • Worker crash before and after mutation.
  • Provider outage and eligible pre-action fallback.
  • Budget, cycle, fan-out, cancellation, and deadline stops.
  • Prompt-injected document and compromised-tool simulation.
  • Redaction canary absent from telemetry.
  • Rollback to prior release manifest.

Production checkpoint: Record a demo evidence pack: threat model, policy matrix, architecture decision records, event timeline, trace, evaluation report, load/chaos report, SBOM/image scan, approval record, reconciliation proof, dashboard, and runbook exercise.

How does it fail safely?

If required evidence, policy, capability, provider, storage, or approval is unavailable, the run becomes blocked, queued, limited, or failed. It never silently downgrades to ungrounded prose or unapproved action.

What mistakes should you avoid?

  • Integrating live providers before fake-adapter contracts pass.
  • Treating the happy path as the capstone.
  • Building one universal tool with internal string commands.
  • Shipping without operator cancellation and quarantine switches.
  • Declaring success before recovery and adversarial evidence exists.

Check your understanding

  1. Why is the fake adapter built first?
  2. When may the router switch providers safely?
  3. Which artifacts prove production readiness?

Expert extension

Present the evidence pack to a reviewer who did not build the system. Ask them to trace one side effect and one failure end-to-end. Any unanswered question becomes a backlog item.

Official references

  • OpenAI Agents SDK
  • Claude Agent SDK