Failure-First AI Agents: Designing Timeouts, Fallbacks, and Human Handoffs That Don't Break Prod
How to build agents that fail safely instead of failing loudly.
Category: AI
15 posts found
Tool-Safe AI Agents: Practical Guardrails for Real-World Integrations
How to stop your agents from doing unsafe or surprising things when they call tools like email, payments, or internal APIs.
Budget-Aware AI Agents: Keeping Cost, Tokens, and Latency Under Control
How to stop agents from looping forever, burning tokens, and slowing everything down.
Tracing AI Agents: Logging, Replay, and Debugging for Tool-Using Workflows
How to see what your agent actually did, step by step, and debug it like normal software. A practical guide to logging, replaying, and debugging AI agent workflows.
Closing the Loop: Building Practical Feedback Loops for LLM Apps in Production
How to capture real user behavior and use it to quietly improve LLM prompts, tools, and policies over time. A practical guide to building feedback loops that turn messy usage into structured improvement.
Hard Boundaries for AI Agents: Time, Budget, and Permission Controls in Production
How to stop agents from running wild: clear limits on time, tokens, tools, and user data. Practical patterns to keep AI agents under control in production systems.
Schema-First LLM Apps: Designing JSON and Tool Calls Before Prompts
How to treat LLMs as strict, structured components instead of free-form text generators. Start with schemas and tools, then write prompts around them.
Defending Against Prompt Injection: Building Secure LLM Pipelines
Learn how to design prompt pipelines that defend against adversarial inputs like prompt injection, malicious context, and out-of-distribution queries. Build production-ready LLM systems with proper input sanitisation, role separation, and monitoring.
Efficient Context-Window Management: Best Practices for Long-Form Retrieval-Augmented LLM Applications
Learn how to optimize context-window usage and retrieval-augmented generation pipelines when working with long documents. Covers chunking strategies, context budgeting, embedding retrieval, caching, and cost-performance trade-offs.
Observability & MLOps for LLMs: From Metrics to Anomaly Detection in Production
Production LLM systems need observability. Learn how to monitor prompts, track token usage, detect drift, catch hallucinations, and build alerting systems tailored for LLM workflows.