Troubleshooting & Optimization
Diagnosing and fixing the things that go wrong — loops, hallucinations, latency, and runaway cost.
Why Your AI Agent Keeps Hallucinating
Why your AI agent keeps hallucinating and how to fix it: the real causes of fabricated answers and concrete steps to ground your agent in reliable information.
How to Fix an AI Agent Stuck in a Loop
How to fix an AI agent stuck in a loop: the common causes of repetitive behavior and practical steps to break loops and keep your agent making progress.
Debugging Tool-Calling Errors in AI Agents
Debugging tool-calling errors in AI agents: how to diagnose and fix the common failures when agents call functions and APIs, from bad arguments to silent errors.
Why Your AI Agent Ignores Instructions
Why your AI agent ignores instructions and how to fix it: the real reasons agents drift from their prompts and practical ways to make them follow directions.
How to Reduce AI Agent Latency
How to reduce AI agent latency: practical techniques to make agents respond faster, from cutting steps and model calls to caching, parallelism, and smarter design.
Optimizing Token Usage in AI Agents
Optimizing token usage in AI agents: practical ways to cut wasted tokens, control costs, and keep context lean without hurting your agent's performance.
Why Your Multi-Agent System Is Failing
Why your multi-agent system is failing and how to fix it: the common causes of coordination breakdowns and practical steps to make multiple agents work together.
How to Improve AI Agent Accuracy
How to improve AI agent accuracy: practical, framework-agnostic techniques to make agents more correct and reliable, from grounding and prompting to evaluation.
Fixing Memory Issues in AI Agents
Fixing memory issues in AI agents: how to diagnose and resolve forgetting, context overflow, and stale or wrong recall so your agent remembers what matters.
Why Your Agent Picks the Wrong Tool
Why your agent picks the wrong tool and how to fix it: the real causes of poor tool selection and practical steps to help your agent choose correctly every time.
How to Handle Rate Limits in AI Agents
Learn how to handle rate limits in AI agents with retries, backoff, batching, and request budgeting to keep workflows running smoothly under load.
Troubleshooting Agent Context-Window Overflows
Troubleshoot agent context-window overflows with trimming, summarization, and retrieval strategies that keep prompts within model limits without losing key facts.
How to Make AI Agents More Reliable
Learn how to make AI agents more reliable with validation, retries, structured outputs, observability, and guardrails that prevent silent failures in production.
Why Your Agent's Output Is Inconsistent
Discover why your agent's output is inconsistent and how sampling settings, vague prompts, and unstable context cause variability, plus concrete fixes.
How to Speed Up Slow AI Agents
Learn how to speed up slow AI agents by reducing model calls, parallelizing work, streaming output, and caching, so latency drops without hurting quality.
Reducing Costs in Production AI Agents
Practical ways of reducing costs in production AI agents through model tiering, prompt trimming, caching, and call reduction without sacrificing output quality.
How to Fix Broken Agent Workflows
Learn how to fix broken agent workflows by isolating the failing step, repairing handoffs, adding validation, and building recovery paths for multi-step agents.
Why Your RAG Agent Returns Irrelevant Results
Find out why your RAG agent returns irrelevant results and how chunking, embeddings, query phrasing, and retrieval tuning fix poor relevance in retrieval.
How to Improve Agent Planning Quality
Learn how to improve agent planning quality with clearer goals, decomposition, grounded tool awareness, and plan validation so agents tackle complex tasks reliably.
Troubleshooting Authentication Failures in Agent Tools
Troubleshoot authentication failures in agent tools by diagnosing expired tokens, missing scopes, and credential handling, with fixes for reliable tool access.
How to Prevent Agent Infinite Recursion
Learn how to prevent agent infinite recursion with iteration caps, loop detection, clear stopping conditions, and progress checks that keep agents from spinning forever.
Why Your Agent Times Out and How to Fix It
Understand why your agent times out and how to fix it by trimming long call chains, parallelizing work, tuning timeouts, and streaming progress to users.
Optimizing Vector Search for Agent Memory
Learn techniques for optimizing vector search for agent memory, including embeddings, indexing, filtering, and retrieval tuning that improve recall and speed.
How to Handle Conflicting Agent Outputs
Learn how to handle conflicting agent outputs using voting, verification, source grounding, and resolution rules so multi-agent and multi-run systems stay consistent.
Fixing Prompt Drift in Long-Running Agents
Learn about fixing prompt drift in long-running agents by reinforcing instructions, managing context, and resetting state so agents stay on task over many turns.
How to Reduce Agent Error Rates
Learn how to reduce agent error rates by categorizing failures, validating outputs, hardening tools, and feeding errors back so agents self-correct and improve.
Why Your Agent Can't Complete Complex Tasks
Discover why your agent can't complete complex tasks and how decomposition, better planning, context management, and tool design unlock harder multi-step work.
How to Tune Agent Temperature and Parameters
Learn how to tune agent temperature and parameters like top-p and max tokens to balance consistency and creativity across different agent tasks and steps.
Troubleshooting Multi-Agent Communication Breakdowns
Troubleshoot multi-agent communication breakdowns caused by ambiguous messages, lost context, and unclear roles, with fixes for reliable agent-to-agent coordination.
How to Optimize Agent Prompt Length
Learn how to optimize agent prompt length by trimming redundancy, managing history, and filtering context to cut cost and latency while preserving accuracy.
Why Your Agent Produces Unsafe Actions
Learn why your agent produces unsafe actions, the common root causes behind risky behavior, and concrete fixes to keep autonomous systems reliable.
How to Improve Agent Decision-Making
Practical techniques to improve agent decision-making, from better context and structured reasoning to evaluation loops that make autonomous agents reliable.
Fixing Slow Vector Database Queries for Agents
Diagnose and fix slow vector database queries for agents, covering indexing, filtering, embedding size, and infrastructure tuning for fast retrieval.
How to Make Agents Handle Edge Cases
Learn how to make agents handle edge cases gracefully, with strategies for unexpected inputs, failures, and ambiguity that keep autonomous systems robust.
Performance Tuning for Production Agent Systems
A practical guide to performance tuning for production agent systems, covering latency, cost, concurrency, and reliability for fast, efficient agents.