You will get your AI agent hardened for production (top risks fixed)
Project details
You have an audit report - from my production-readiness audit or your own review - and a list of known risks. This sprint turns that list into shipped fixes.
Available following a production-readiness audit — or bring an equivalent risk map and I'll scope the sprint from it. I don't quote fixed-price remediation on a codebase I haven't read.
I run a Claude-in-the-loop agent in unattended production with real money at stake, 24/7. The hardening patterns I install are the ones keeping that system alive: fail-closed guardrails (every LLM failure maps to a safe outcome), hard cost caps with bounded retries, alerting that escalates and is itself monitored, idempotent side effects across retries and restarts, and rehearsed rollback.
How it works: we agree the top 5-8 risk items and the verification bar for each. Each lands as a reviewable PR: the failure it closes, how to verify it, and a rollback path. New items found mid-sprint are quoted as add-ons, never absorbed silently. Handover: green test suite, end-to-end alert verification, rollback drill, and a 30-minute walkthrough.
Scope: one repo, one deployment target, one agent system, 7 days. Extra risk items available as an add-on.
Available following a production-readiness audit — or bring an equivalent risk map and I'll scope the sprint from it. I don't quote fixed-price remediation on a codebase I haven't read.
I run a Claude-in-the-loop agent in unattended production with real money at stake, 24/7. The hardening patterns I install are the ones keeping that system alive: fail-closed guardrails (every LLM failure maps to a safe outcome), hard cost caps with bounded retries, alerting that escalates and is itself monitored, idempotent side effects across retries and restarts, and rehearsed rollback.
How it works: we agree the top 5-8 risk items and the verification bar for each. Each lands as a reviewable PR: the failure it closes, how to verify it, and a rollback path. New items found mid-sprint are quoted as add-ons, never absorbed silently. Handover: green test suite, end-to-end alert verification, rollback drill, and a 30-minute walkthrough.
Scope: one repo, one deployment target, one agent system, 7 days. Extra risk items available as an add-on.
AI Algorithms
Large Language ModelAI Applications
AI Chatbot, AI-Generated Code, Conversational AIAI Development Language
PythonAI Models
ChatGPT, GPT-4, LLaMAWhat's included $3,500
These options are included with the project scope.
$3,500
- Delivery Time 7 days
- Model Testing & Optimization
Optional add-ons
You can add these on the next page.
Extra risk item fixed
+$500Frequently asked questions
About Amit
Claude Code Engineer | I Take AI Agents from Demo to Production
Seabrook, Australia - 7:52 am local time
What I do: take AI agents from "works in a demo" to monitored, unattended production.
• Fail-closed guardrails — every LLM failure (timeout, refusal, malformed output, provider error) maps to a safe outcome by design, not by accident
• Cost circuit breakers — per-call metering and attribution, hard budgets that fail closed, bounded loops and retries
• Observability — every decision journaled with its inputs; intent-vs-reality reconciliation; alerts that repeat and escalate; monitoring that is itself monitored
• Out-of-process protection — safety that survives the process dying
• Regression gating — the deterministic layer around the model (gates, parsers, sizing, routers) unit-tested with the LLM stubbed, 934 tests gating every deploy, including agent-authored code
• Rollback & kill switches — mode changes that beat in-flight work, human-approved deploys, tested rollback
My method is public: I maintain an open-source production-hardening checklist for LLM agents, and a full write-up of my own live system — incidents included. Both are in my portfolio below.
FIXED-SCOPE OFFER — Production-Readiness Audit ($1,500–2,000): your repo + deployment reviewed against that public checklist. Written report, prioritized punch-list of the 5–8 highest-risk gaps, 30-minute walkthrough call. 3 business days. Scope: one repo, one deployment target, one agent system.
Also available for: agent-hardening sprints, white-label delivery for agencies shipping Claude/AI-agent builds, and production-ops retainers.
Stack: Python, FastAPI, Claude API (structured outputs, tool use, prompt caching, multi-provider failover), Claude Code, Docker, Caddy, SQLite/SQLAlchemy, Linux VPS operations, webhook and Telegram integrations.
Steps for completing your project
After purchasing the project, send requirements so Amit can start the project.
Delivery time starts when Amit receives requirements from you.
Amit works on your project following the steps below.
Revisions may occur after the delivery date.
Scope the punch list
We agree the 5-8 risk items in scope and the verification bar for each: tests passing, alerts firing, rollback rehearsed.
Fix in PR-sized changes
Each risk lands as a reviewable PR with tests: fail-closed defaults, cost caps, alerting, idempotency, rollback paths.