Apna Agent Harness Cloud par Deploy Karein: Multi-Track Crash Course
*17 Concepts • Four learning tracks. Reader track: 3-4 ghante conceptual reading. Beginner / Intermediate / Advanced tracks: 1-2 din, 3-5 din, 7-10 din. Lab se pehle apna track choose karein.*
Pichle courses mein aap ne agents banaye, lekin woh aksar laptop par hi chal rahe thay. Yeh course un agents ko real cloud service banata hai: users internet se harness ko call karte hain, state database mein rehti hai, files object storage mein rehti hain, aur risky code sandbox mein chalta hai. End par aap ke paas live harness hota hai aur aap ko har layer ka kaam samajh aata hai.
Teen terms pehle samajh lein.
- Harness. Agent ka brain aur controls: loop, tool routing, secrets, state, audit log. Yeh generated code khud nahin chalata.
- Sandbox. Locked-down workspace jahan agent ka generated code run hota hai. Is ke paas harness ke secrets ya database string nahin hoti.
- Manifest. Short description ke sandbox ko kaun si files, storage mounts, aur capabilities chahiye.
Plain-English version: earlier courses ne AI-native company ka architecture banaya. Is course ka kaam us architecture ko cloud address dena hai. Aap agent ka brain managed cloud runtime par rakhte hain, memory Neon Postgres mein, files Cloudflare R2 mein, aur code execution separate sandbox mein. Yeh ek complete path hai; alternatives exist karte hain, lekin pehle ek path end-to-end chalana zyada useful hai.
Core sentence: harness control plane hai jo aap own karte aur running rakhte hain; sandbox execution plane hai jo aap create, use once, aur throw away karte hain. Harness keys, state, aur audit rakhta hai. Sandbox risky work karta hai aur secrets nahin rakhta.
Quick Win: 15 minutes mein harness local boot karein
Cloud touch karne se pehle prove karein ke harness laptop par chal sakta hai.
- Companion zip download karein aur unzip karein.
- Folder ko coding agent mein open karein.
AGENTS.mdread karwayen.- Agent se yeh prompt run karwayen:
Read AGENTS.md, then boot Maya's harness locally so I can see it run.
1. Run the SDK probe at the end of AGENTS.md.
2. Install dependencies (`make install`) and copy `.env.example` to `.env`.
3. Start the harness (`make run`).
4. In a second shell, request `GET /health` and show me the exact response.
Done jab /health yeh return kare:
{
"status": "ok",
"model": "gpt-5.4-mini",
"backends": { "postgres": false, "sandbox": false, "r2": false }
}
Yeh response batata hai: harness alive hai, model known hai, aur optional backends abhi wired nahin. Baad ki decisions in flags ko one by one true karti hain.
Four learning tracks
| Track | Time | Aap kya complete karte hain | Kis ke liye |
|---|---|---|---|
| Reader | 3-4 hours | Quick Win, 17 concepts, closing. No cloud accounts. | Leaders, architects, ML platform owners |
| Beginner | 1-2 days | Local harness, SDK probe, scaffold, container. | Engineers jo deployment seekh rahe hain |
| Intermediate | 3-5 days | Cloud deploy, durable state, R2 storage, observability. | Teams jinko live harness chahiye |
| Advanced | 7-10 days | Sandbox, eval suite, CI gate, checklist. | Production teams |
Sprint at a glance
| Day | Focus | Artifact |
|---|---|---|
| 1 | Concepts 1-4 + scaffold | Local FastAPI app |
| 2 | Containerize + deploy | Public harness URL |
| 3 | Neon Postgres | Durable state |
| 4 | Cloudflare R2 | File storage |
| 5 | Shippable checkpoint | Deployed harness |
| 6 | Sandbox | Safe code execution |
| 7 | Observability | Infra and agent traces |
| 8-9 | Evals | CI gate and nightly reports |
| 10 | Checklist | Operable production handoff |
Stack primer
Docker aur containers
Container woh repeatable package hai jisme app, dependencies, aur command same rehte hain. Laptop aur cloud ke darmiyan "works on my machine" drift kam hoti hai.
FastAPI
FastAPI harness ka web layer hai. GET /health state batata hai. POST /runs user task receive karta hai, agent loop run karta hai, aur result/audit persist karta hai.
Neon Postgres
Neon durable state store hai: sessions, runs, traces, artifacts, audit log. App pooled endpoint use karta hai; migrations direct endpoint use karti hain.
Cloudflare R2
R2 files aur artifacts rakhta hai. Harness scoped presigned URLs mint karta hai; sandbox ko root bucket credentials nahin milte.
Part 1: Deployment problem
Concept 1: "Works on my machine" deployment nahin
Laptop run manual hota hai: keys local file mein, state local file mein, code same process mein. Production service ko restart, scaling, logs, audit, secrets, aur internet traffic handle karna hota hai.
Concept 2: Harness/sandbox split
Harness control plane hai. Sandbox execution plane hai. Agar agent generated code chalata hai, woh sandbox mein chalta hai, harness process mein nahin.
Concept 3: SDK ko cloud se paanch surfaces chahiye
- HTTP runtime for harness.
- Durable state.
- File/artifact storage.
- Isolated code execution.
- SDK orchestration jo loop, Manifest, tool routing, aur traces manage kare.
Part 2: Five-component stack
Concept 4: FastAPI as harness web layer
FastAPI simple request boundary deta hai: health, run, status, artifacts. Yeh SDK ko web service shape deta hai.
Concept 5: Azure Container Apps as runtime
Azure Container Apps container ko public URL, autoscale, secrets, revisions, aur scale-to-zero discipline deta hai. Equivalent choices: Cloud Run, Fargate, Fly.io, Kubernetes.
Concept 6: Neon for durable state
Runs, traces, sessions, audit rows, aur artifact metadata database mein rehte hain. Restart se memory lose nahin hoti.
Concept 7: R2 for files
Large inputs/outputs database mein nahin rakhte. R2/S3 style object storage better hota hai; database sirf metadata rakhta hai.
Part 3: Execution plane
Concept 8: Sandbox capabilities
Sandbox shell, filesystem, aur mounted files expose karta hai. Harness secrets sandbox mein leak nahin karta.
Concept 9: Sandbox provider choose karna
Cloudflare Sandbox, E2B, Modal, Daytona, Docker sab possible hain. Choice isolation, cold start, price, network policy, aur provider maturity par depend karti hai.
Concept 10: Harness-to-sandbox handoff
Harness Manifest banata hai, scoped URLs attach karta hai, sandbox run start karta hai, result/artifacts wapas collect karta hai.
from agents.sandbox import Manifest
from agents.sandbox.entries import R2Mount
manifest = Manifest(entries={
"/workspace/input": R2Mount(url=input_url),
"/workspace/output": R2Mount(url=output_url),
})
Part 4: Observability and evals
Concept 11: Observability architectural surface hai
Infrastructure metrics aur agent traces dono chahiye. Shared run_id ke baghair alert se agent behavior tak jana slow hota hai.
Concept 12: Evals architectural surface hai
Eval suite CI mein regression gate banti hai aur nightly behavior report banati hai. Production deploy sirf container push nahin; behavior change bhi measure hota hai.
Part 5: Deployment lab
Decision 0: SDK probe
Installed openai-agents version aur sandbox imports verify karein. Agar brief aur live SDK disagree karein, live SDK win karta hai.
Decision 1: Scaffold harness
FastAPI app banayein. GET /health, POST /runs, local fallback, SQLite fallback, aur no-key boot path maintain karein.
Decision 2: Containerize
Dockerfile production-sized ho, dependencies pinned hon, health check available ho, aur image local run kare.
Decision 3: Deploy to Azure Container Apps
Container registry push, Container App create, secrets set, ingress enable, revision verify.
Decision 4: Wire Neon Postgres
Schema migrate karein: sessions, runs, traces, artifacts, audit log. App pooled URL use kare.
Decision 5: Wire Cloudflare R2
Bucket create karein, lifecycle policy set karein, presigned URL read/write flow verify karein.
Decision 6: Wire sandbox execution
Sandbox disabled path pehle green rakhein. Phir provider key add karein, Manifest compose karein, aur code execution trace karein.
Decision 7: Wire observability
OpenTelemetry spans, app logs, Phoenix/trace visibility, aur shared run_id wire karein.
Decision 8: Wire eval suite
CI regression gate, nightly behavior reports, trace-to-eval pipeline.
Decision 9: Production checklist
Secrets rotation, blue/green deploy, backup, rate limits, cost alerts, on-call runbook.
Honest frontiers
Concept 13: Cost economics
Small harness cheap hota hai. Real cost model inference, sandbox minutes, storage egress, traces, aur eval runs mein dikhta hai.
Concept 14: Multi-region
Multi-region sirf "deploy twice" nahin. State, storage, secrets, latency, data residency, aur failover rules decide karne parte hain.
Concept 15: Recipe se kab migrate karna
Jab throughput, compliance, network isolation, ya org standards require karein, stack badal sakta hai. Architecture split same rehta hai.
Concept 16: Deployment kya solve nahin karta
Deployment wrong prompts, weak evals, poor HITL design, ya bad product judgment solve nahin karta.
Five things not to do
- Harness secrets sandbox mein mat bhejein.
- Database connection string workspace mein mat mount karein.
- Evals ko "later" indefinitely mat rakhein.
- Health endpoint ko fake green mat banayein.
- Local-only assumptions production mein mat carry karein.
Closing
Concept 17: Deployed harness as realization
Agent Factory thesis tab practical hoti hai jab Worker ka harness live ho, state durable ho, risky work isolated ho, aur behavior measurable ho.
Cheat sheet
| Layer | Best first choice | Replaceable alternatives |
|---|---|---|
| Harness web | FastAPI | Express, Hono, Django |
| Runtime | Azure Container Apps | Cloud Run, Fargate, Fly.io |
| State | Neon Postgres | Supabase, RDS, Cloud SQL |
| Files | Cloudflare R2 | S3, Azure Blob, GCS |
| Sandbox | Cloudflare/E2B | Modal, Daytona, Docker |
| Observability | OpenTelemetry + Phoenix | Langfuse, Datadog, Honeycomb |
Quick reference
# Local dev
make install
make run
curl http://localhost:8000/health
# Cloud
az containerapp up
az containerapp logs show
# Tear down
az containerapp delete
Companion download
Download the companion project.
References
- OpenAI, "The next evolution of the Agents SDK," April 15, 2026.
- Azure Container Apps docs.
- Neon Postgres docs.
- Cloudflare R2 docs.
- OpenTelemetry docs.