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 Agents
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Budget-Aware AI Agents: Keeping Cost, Tokens, and Latency Under Control
How to stop agents from looping forever, burning tokens, and slowing everything down.
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.
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.
From Prompt-Chaining to Prompt-Graph: Next-gen Workflow Design for LLM-Powered Pipelines
Move beyond linear prompt chains to graph-based workflows. Learn how to design robust LLM pipelines with branching, error handling, and observability using prompt-graph architecture.
Adaptive Role Negotiation in Multi-Agent Systems
How autonomous AI agents can dynamically negotiate and adapt roles in real-time collaborations using LLM-driven role arbitration and reinforcement learning. Practical guide to building self-organizing agent teams.
Memory Sharding for Scalable Agent Collectives
Building distributed cognitive memory for multi-agent ecosystems. Learn how memory sharding solves context collisions, slow retrieval, and redundant embeddings in large-scale agent systems.
Reflexive AI Agents: Closing the Loop Between Perception and Self-Evaluation
How AI agents can evaluate and adjust their own reasoning loops dynamically using internal reflection models. Practical guide to building agents that self-correct and adapt in real-time.
Agentic Shadow Models — Reducing Latency via Local Predictive Replicas
How to build lightweight local replicas that predict remote agent responses to cut coordination latency in multi-agent workflows. Practical guide to shadow models with Python examples.
Vector-Space Protocols — Building Shared Context Between AI Agents
How AI agents can communicate through shared vector spaces instead of brittle API schemas. Learn to build Vector-Space Protocols (VSPs) that enable semantic interoperability and emergent cooperation in multi-agent systems.