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The Senior Engineer's AI Agent Reference: 40 Production Architectures with Failure Modes, Cost Benchmarks, and Observability Runbooks
Paperback
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ISBN13: 9798195083748
Publisher: Independently Published
Published: Apr 30 2026
Pages: 156
Weight: 0.62
Height: 0.33 Width: 7.00 Depth: 10.00
Language: English
Publisher: Independently Published
Published: Apr 30 2026
Pages: 156
Weight: 0.62
Height: 0.33 Width: 7.00 Depth: 10.00
Language: English
Your AI agent just spent four hours and $4,147 trying to refund a $19 subscription. It never escalated, never gave up - just kept retrying the same failing API call, burning tokens on confident reasoning traces that led nowhere.
This is what production agent failures actually look like: not hallucinations or refusals, but confident, well-formatted infinite loops no one saw coming because the demos worked fine.
This is what production agent failures actually look like: not hallucinations or refusals, but confident, well-formatted infinite loops no one saw coming because the demos worked fine.
Most engineers building AI agents today have never paid an LLM bill for an agent that ran thirty days under real load. The patterns that survive production traffic, vendor outages, cost reviews, and security audits aren't in research papers. They're in this book.
THE SENIOR ENGINEER'S AI AGENT REFERENCE is a working catalogue of 40 production agent architectures that account for most agent deployments at scale - plus the 47 failure modes that knock them over, the observability substrates that catch failures early, and cost benchmarks for sober tradeoffs before quarterly reviews. INSIDE YOU'LL FIND:
If you're responsible for an agent that runs in production (not a demo), handles real queries or takes real-world actions, must pass security review or regulatory audit, and will be measured on uptime, accuracy, latency, and cost - this is the reference you need. BY THE TIME YOU FINISH THIS BOOK, YOU WILL KNOW:
Don't wait for the post-mortem. Get the reference. Build it right the first time.
THE SENIOR ENGINEER'S AI AGENT REFERENCE is a working catalogue of 40 production agent architectures that account for most agent deployments at scale - plus the 47 failure modes that knock them over, the observability substrates that catch failures early, and cost benchmarks for sober tradeoffs before quarterly reviews. INSIDE YOU'LL FIND:
- 40 production-ready agent patterns organized by problem - decision-loop agents, memory architectures, RAG agents, tool-use agents, supervisors, multi-agent collaboration, workflow orchestration, self-improving systems, and evaluation frameworks
- 47 failure-mode codes (F01-F47) with diagnosis runbooks, remediation paths, and real incident patterns from financial services, healthcare technology, legal research, government contractors, and B2B SaaS at scale
- Cost and latency benchmarks for every pattern - per-invocation costs, p50/p95/p99 latency distributions, monthly burn rates at production load, and tradeoffs between accuracy, speed, and expense
- Observability runbooks showing which metrics to track, alerts to set, traces to log, and how to instrument agents so you know what's happening before users tell you it's broken
- Security hardening for agents taking real-world actions - prompt injection defenses, sandboxing strategies, audit trails, and compliance constraints for HIPAA, SOC 2, FedRAMP, and PCI
- Reference implementations in Python for every pattern - actual code you'd write to make patterns work, with inline comments explaining the decisions that matter
If you're responsible for an agent that runs in production (not a demo), handles real queries or takes real-world actions, must pass security review or regulatory audit, and will be measured on uptime, accuracy, latency, and cost - this is the reference you need. BY THE TIME YOU FINISH THIS BOOK, YOU WILL KNOW:
- Which agent pattern fits your problem (and which patterns are theatre)
- How to instrument agents so failures are visible before they compound
- What agents cost at 10K, 100K, and 1M requests/day
- How to design retry policies, idempotency guarantees, and escalation paths that don't burn money
- Which security controls to layer on before agents touch production data
- How to evaluate accuracy without fooling yourself with correlated judges or contaminated test sets
Don't wait for the post-mortem. Get the reference. Build it right the first time.
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