AI agent stuck in loop or not completing tasks.Fixed this session.

PM assigned in 10 minutes. AI agent specialist starts diagnosing within the hour. Agent infinite loops are caused by four patterns: a tool call that consistently returns an error the agent interprets as a reason to retry rather than escalate (fix: add explicit error handling that terminates the loop after N failures); a goal that cannot be achieved with the available tools so the agent keeps searching (fix: add a maximum step limit and graceful failure response); a memory management issue where the agent loses track of what it already tried and repeats the same failed action (fix: restructure agent state to track attempted actions); or a prompt that does not tell the agent when to stop (fix: add explicit termination conditions in the system prompt). The engineer traces the agent's tool call history to identify which pattern applies.

Production-only agent failures are a very common pattern test datasets are clean and structured, production data is messy and unpredictable. The most common causes: production tool calls return data in a format the agent was not designed to handle (extra fields, nested objects, null values where strings were expected), production context windows are longer because real conversations are longer than test conversations causing the agent to lose instructions buried in the middle, real user queries are more ambiguous than test queries causing wrong tool selection decisions, and production rate limits or API timeouts cause tool failures the agent does not handle gracefully. The engineer runs failing production queries through the agent with detailed tracing enabled, identifies the specific failure pattern, and implements fixes including input normalisation, improved error handling, or instruction restructuring.

Yes. A full agent rebuild is a Full Day to Sprint Pack engagement. The engineer designs the new architecture using the appropriate framework: LangGraph for stateful multi-step agents needing explicit control over the execution graph and failure recovery, CrewAI for multi-agent systems where specialist agents collaborate, AutoGen for conversation-based workflows, or direct tool-calling using the Anthropic or OpenAI API for cases where framework overhead adds complexity without benefit. PM assesses which architecture matches your use case before any code is written. The rebuild includes a test suite covering your real production failure scenarios so you know the new agent handles them correctly before going live no surprises after deployment.

Yes. QuickHire has engineers specialising in CrewAI, LangGraph, AutoGen, and custom agent frameworks. For CrewAI: agent role and goal design (agents perform significantly better when roles have clear, non-overlapping responsibilities), task dependency management ensuring tasks execute in the correct order with outputs passed correctly between agents, tool integration connecting CrewAI agents to external APIs, databases, or MCP servers, memory configuration (short-term conversation vs. long-term entity vs. external knowledge base), and debugging agent decision traces to understand why an agent took a wrong action path. The engineer works with your existing CrewAI version and adds debugging and observability tooling needed to diagnose production failures systematically.

PM delivers a monitoring and observability setup as part of the fix. This includes: structured logging of every agent step (tool name, input, output, step duration) so you can replay a failing agent run offline, a maximum step limit with a graceful failure response so the agent never loops indefinitely again, an alerting rule that fires when an agent run exceeds the expected step count as an early warning before users see a timeout, and a test suite of your known production failure scenarios that runs in CI so future agent changes are validated before deployment. If you use LangSmith, LangFuse, or Weights and Biases, the engineer integrates the tracing into your existing observability platform rather than introducing new tooling.

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