Every post below is driven by data we can point at on disk — our own dogfood trip log first, then the loops, context overflows, and budget breaches our SDK catches inside customer apps. If a post's data isn't there yet, it stays gated. No vibes; no fabricated stats.
Cadence is a target, not a contract. If the gate hasn't opened, we slip the day rather than ship filler. The credibility of the 30-day series is the whole point.
The Realtime API bills partial audio even on interruption — barge-in amplification loops, server VAD false-positive storms, function call echo chambers, and session transcript accumulation will drain your gpt-4o-realtime-preview budget invisibly. Four Python circuit breakers with complete implementations.
Local models skip the billing dashboard — but VRAM OOM crash loops, silent context truncation causing tool-call repetition, cold-start cascades from model-reload thrash, and CPU inference runaway are just as expensive. Four failure modes unique to Ollama and llama.cpp agents with complete Python guard implementations.
The Converse API unifies tool use across every Bedrock model behind a single boto3 call — but you own the messages list, the loop, and the budget. Four failure modes — tool call spirals, conversation history explosion, cross-model retry amplification, and streaming accumulation traps — with a complete Python ConverseBreaker implementation.
invoke_inline_agent lets you define an agent’s foundation model, instructions, and action groups dynamically at runtime. Four cost failure modes absent from standard Bedrock Agents — instruction loops, session accumulation, action group thrash, and supervisor cascades — with complete Python InlineAgentBreaker implementation.
Building agentic loops directly on the Anthropic Messages API means no framework guardrails between you and the billing meter. Four failure modes — tool use spirals, context window accumulation, retry cascade multiplication, and budget breach — with complete Python and TypeScript circuit breaker implementations using the Anthropic SDK.
Building agentic loops directly on the Gemini API means no ADK guardrails between you and the bill. Four failure modes — function call spirals, chat history accumulation, parallel call multiplication, and retry cascades — with complete Python guards using the google-genai SDK.
Every AI agent framework creates the same four failure modes: tool call spiral, context accumulation, retry cascade, and budget breach. Universal Python and TypeScript detection patterns with a complete index of all 29 framework-specific guides in this series.
Dify’s ReAct agent and Chatflow visual builder have no built-in circuit breaker. Four failure modes — agent tool call spiral, Chatflow LLM context accumulation, Iteration node runaway, and HTTP Request retry cascade — with full Python Code node guard implementations.
Flowise’s LangChain.js-based agent executor and Agentflow v2 LangGraph state machine have no built-in circuit breaker. Four failure modes — tool call spiral, multi-agent supervisor loop, context history accumulation, and HTTP retry cascade — with full Custom Function node guard implementations.
Microsoft Copilot Studio has no built-in circuit breaker. Four failure modes — topic redirect cycles, Power Automate retry storms, generative AI knowledge search spirals, and autonomous agent tool call loops — with full guard implementations in Power Fx, Power Automate expressions, and TypeScript custom connectors.
Salesforce Agentforce’s Atlas reasoning engine has no built-in circuit breaker. Four failure modes — action call spiral, write action idempotency failure, Data Cloud retrieval context avalanche, escalation retry deadlock — with full Apex guard implementations for @InvocableMethod actions and Platform Cache session state.
IBM watsonx.ai’s agent framework runs a ReAct loop with no built-in circuit breaker. Four failure modes — tool call invocation spiral, nested agent chaining, RAG retrieval context avalanche, Granite model retry storm — with full Python guard implementations for the watsonx.ai Python SDK.
n8n’s AI Agent node runs a LangChain agentic loop with no built-in circuit breaker. Four failure modes — tool call invocation spiral, sub-workflow recursion, Window Buffer Memory context accumulation, HTTP Request retry cascade — with full JavaScript Code node guards you can drop into any n8n workflow.
Vercel AI SDK’s maxSteps counts agentic steps but can’t detect tool call invocation spirals, parallel tool call cost amplification, cross-step context window drift, or provider-fallback re-routing loops. Four failure modes with a full TypeScript AISdkBreaker circuit breaker wrapping tool execute functions.
Spring AI’s maxToolCallsPerRequest counts tool calls but can’t detect function callback invocation spirals, MessageChatMemoryAdvisor token inflation, VectorStore RAG query fixation, or multi-agent task delegation loops. Four failure modes with a full Java SpringAgentBreaker circuit breaker using the CallAroundAdvisor API.
IBM’s Bee Agent Framework maxIterations counts turns but can’t detect tool observation fixation spirals, ReAct reasoning echo loops, memory token drift, or nested sub-agent back-delegation cycles. Four failure modes with a full TypeScript BeeAgentBreaker circuit breaker using Bee’s native event emitter API.
Vertex AI Agent Builder’s session limits count turns, not patterns. Four failure modes — playbook tool invocation spiral, data store grounding query fixation, multi-playbook escalation loop, session context token drift — with a full Python VertexAgentBreaker circuit breaker wrapping the Dialogflow CX SDK.
Azure AI Agent Service’s max_completion_tokens caps token spend but can’t detect run-step tool-call spirals, file search query fixation, thread token drift, or connected-agent re-delegation loops. Four failure modes with a full Python AzureAgentBreaker circuit breaker wrapping the azure-ai-projects SDK.
AWS Bedrock Agents’ maxLength counts steps but can’t detect action group invocation spirals, knowledge base RAG query fixation, multi-agent supervisor cascades, or session token drift. Four failure modes with a full Python BedrockBreaker circuit breaker wrapping boto3 invoke_agent.
Letta’s max_steps counts turns but can’t detect archival memory search spirals, core memory contradiction rewrite loops, recall pagination deadlocks, or multi-agent message ping-pong. Four failure modes unique to Letta’s stateful memory architecture, with full Python LettaBreaker circuit breaker.
DSPy’s max_backtracks counts assertion retries but can’t detect cascade storms, ReAct tool-signature stagnation, multi-hop retrieval query fixation, or compiled demo token bloat. Four failure modes with full Python circuit breaker via GuardedDspyModule, GuardedReAct, and a pre-flight demo audit.
Agno’s max_steps counts steps but can’t detect Team back-delegation cycles, structured output regeneration loops, tool-retry multiplication storms, or storage session bloat. Four failure modes with full Python circuit breaker via GuardedAgent and GuardedTeam subclasses.
smolagents’ max_steps counts steps but can’t detect CodeAgent code-repair loops, tool repetition storms, ManagedAgent delegation cycles, or memory inflation. Four failure modes with circuit breaker via step_callbacks and a lightweight MultiStepAgent subclass.
Haystack’s max_agent_steps counts steps but can’t detect pipeline back-edge cycles that never converge. Four failure modes — non-converging iterative refinement loops, tool repetition storms, chat history token inflation, cross-pipeline delegation depth — with full circuit breaker as a custom Component wrapper and HALF_OPEN recovery.
LlamaIndex’s max_iterations counts steps but can’t see progress. Four failure modes — ReAct reasoning cycles, tool call storms, multi-agent back-delegation, chat history token inflation — with full circuit breaker via CallbackManager event hooks and HALF_OPEN recovery.
ADK’s max_iterations counts turns but can’t see progress. Four failure modes — LoopAgent non-progress, subagent back-delegation cycles, session event log inflation, ParallelAgent over-spawn — with full circuit breaker via ADK’s native callback hooks and HALF_OPEN recovery.
Pydantic AI’s UsageLimits caps volume but can’t see patterns. Four failure modes — result validation retry cascades, tool call storms, nested agent recursion, message history cost drift — with full circuit breaker wrapping Agent.run() and contextvar-based nesting depth tracking.
SK’s TerminationStrategy evaluates the latest message, not the pattern of messages across turns. Four failure modes — AgentGroupChat selection cycles, plugin re-invocation storms, Process Framework circular transitions, chat history cost inflation — with full circuit breaker wrapping AgentGroupChat.invoke() and HALF_OPEN recovery.
AutoGen’s max_consecutive_auto_reply resets every time a different agent speaks — it can’t see speaker cycles in GroupChat. Four failure modes — speaker cycles, nested conversation cascades, code execution storms, message history explosion — with full circuit breaker using register_reply and HALF_OPEN recovery.
CrewAI’s max_iter counts iterations per task, not across the crew. Four delegation-model failure modes — delegation loops, tool storms, manager over-decomposition, memory context drift — with full circuit breaker using step_callback and HALF_OPEN recovery.
The Agents SDK’s max_turns counts turns, not handoff cycles or context accumulation drift. Four failure modes specific to the handoff model — cycle loops, tool storms, context blowout, budget blindness — with full circuit breaker implementation using AgentHooks and HALF_OPEN recovery.
LangGraph’s recursion_limit catches textbook loops but misses the four expensive production failures: supervisor misrouting, state accumulation drift, map-reduce retry storms, and semantic convergence failures. Full circuit breaker with budget tracking, conditional guard edges, and HALF_OPEN recovery.
Running asyncio.gather() over ten agents? A synchronized retry storm or fan-out misconfiguration can 10x your bill in minutes. Four failure modes — retry storm, uncoordinated fleet budgets, unbounded fan-out, uncancelled in-flight tasks — with asyncio-safe code for each.
How we went from $4,200/month to $1,218/month for the same user load — without changing models, degrading quality, or cutting features. Six production-proven patterns: tool result sizing, conversation history flattening, model routing, loop detection, prompt caching, and budget ceilings. Real numbers, real code.
Three Python-specific failure modes (tool-call signature lock, context accumulation drift, async retry collision), two built-in approaches that don't work, and a full CLOSED/OPEN/HALF-OPEN state-machine implementation you can drop into any Python agent today. LangChain and CrewAI integration included.
Four root causes of TypeScript agent loops, three native approaches that look like fixes but aren't, and one pattern-based solution that stops the loop before it fires a third time. With working code for LangChain.js and LangGraph.js compositions.
The dogfood story: our own launch script looped against a shared upstream infra blocker. We instrumented the detector between failures. By the time the script retried a seventh time, the SDK opened the breaker before the API call went out.
Signups, installs, referrers, and star counts at the 24-hour mark — with delta columns against the launch hour. Honest about whether the launch sustained or fizzled.
A customer's agent looped. Our SDK opened the breaker. What the signature looked like, what the breaker defaults were, and what the customer's retry logic did next — anonymized, with permission.
Pattern-matching across 72 hours of customer trips. Categorized by trigger kind (loop / budget / context) and then by signature shape. One redacted example per category — code blocks, not prose.
When the breaker shouldn't have opened. What the user's legitimate workflow looked like, which default exposed the false positive, and whether we're shipping a version bump or a doc clarification.
Five days of npm install @runguard/sdk vs pip install runguard. Two integers, one ratio, and three plausible explanations — not a "the Python community prefers X" from a week of data.
The IDENTITY headline — "How we caught $X in runaway agent runs" — with the math shown. Customer-reported dollar figures where shared, token-pricing estimates otherwise, and every line tagged so readers can audit.
Three concrete things we'd do differently, with the planned fix for each. One thing we got right and want to keep. Honest about cadence — did the gates hold, did we slip a day, did we publish anyway and now regret it?
After day-7, the 30-day promise continues at a weekly cadence. Three stubs already scaffolded; each gates on real data so the structure matches what we've actually seen rather than what we imagined on day 0.
Day-8 to day-14 in numbers — the second week of catches, framed as its own window rather than a "week-1 vs week-2 growth chart." Trip counts by trigger kind, install velocity, the first new signature week-1 didn't carry.
A signature that doesn't appear anywhere in the first 14 days of trip rows — shown raw, walked through the detector that caught it, with the customer's surrounding context (anonymized, with consent). One pattern per post.
The IDENTITY kill-criteria audit, made public. Verdict first; math second; one customer interview as the body; cadence audit of the entire 30-day arc. Publishing the threshold result honestly even if it's "kill" is the trust contract.
First post drops the hour RunGuard's launch channel fires. Stay close — or join the waitlist and we'll email when the SDK ships and the log starts.
