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.
Botpress Cloud bills per LLM invocation as AI credits. Four failure modes that exhaust your credit allocation invisibly: AI Task retry loops where a validation condition that cannot be satisfied for a class of inputs causes the LLM to be called N times per failing session (no order number in the message = no order number extractable, regardless of retries), bot-to-bot handoff cycles where the Orchestrator routes between specialized bots whose fallback conditions send the conversation in a circle (6 routing decisions + 6 intent classifications = 12 credits for zero user value), Knowledge Base re-query fan-out when a Search KB node sits inside a confidence-check loop with an unsatisfiable threshold (each iteration = 1 embedding call + 1 synthesis call, both billed), and autonomous agent action spirals where the planning model repeatedly selects the same Action because the required tool is absent from the action set. Guards: AITaskRetryGuard (input pre-check + per-session retry cap), BotHandoffCycleGuard (visit count per bot + total handoff ceiling with chain log), KBReQueryGuard (topic-hash dedup, cap at 2 queries per topic per session), and AgentActionSpiralGuard (consecutive same-action detector + total-turn ceiling with action distribution log).
ServiceNow Now Assist charges per generative AI invocation across ITSM, HR, CSM, and Flow Designer workflows. Four failure modes that exhaust your Now Assist credits invisibly: business rule update recursion where an AI-generated field write calls current.update() on the same record and re-fires the same business rule (up to 30 levels before the platform stops it, one credit per level), Flow Designer ForEach fan-out where an unbounded "Look Up Records" action passes thousands of records to a "Generate Now Assist Text" loop body, cross-table cascade where Flow A writing to a Problem record fires Flow B which writes back to the originating Incident and re-fires Flow A (invisible in either flow's execution log), and scheduled job concurrency overlap where a batch enrichment job running beyond its interval triggers a second instance that independently calls Now Assist on the same unprocessed records. Guards: BusinessRuleRecursionGuard (hash-based write-back idempotency), FlowForEachGuard (record count pre-flight + hourly execution ceiling), CrossTableCascadeGuard (root trigger ID threading across tables), and ScheduledJobLock (mutex with 8-hour watchdog expiry).
Power Automate's AI Builder charges per AI credit per action call — not per flow run. Four failure modes that exhaust your monthly credit allocation: Apply to Each fan-out where a SharePoint list with 2,000 items × 3 credits per AI Prompt = 6,000 credits from a single flow execution, parallel branch multiplication where branch count multiplies credits on every trigger event, child flow recursion where a Run a Child Flow action writes AI-generated output back to the triggering SharePoint list and re-fires the parent flow indefinitely, and scheduled trigger overlap where a flow that takes 40 minutes on a 30-minute recurrence queues permanently growing instances each independently burning the full credit load. Guards: ApplyToEachGuard, ParallelBranchGuard, ChildFlowWriteBackGuard, and ScheduledFlowLock.
Slack's Events API delivers one HTTP POST per workspace event — wire an LLM call to it without guards and an active channel can exhaust a month's API budget in hours. Four failure modes specific to Slack AI-powered apps: message event fan-out where a message event subscription in a workspace with 1,000 messages/day triggers 1,000 potential LLM calls before any filtering, bot self-loop where a missing subtype === "bot_message" filter causes the bot to respond to its own output indefinitely (3,600 LLM calls/hour at 1s/response), 3-second timeout retry duplication where slow LLM responses cause Slack to retry delivery up to 3× — tripling LLM call volume from a single event without idempotency — and Workflow Builder AI step thundering herds where concurrent workflow executions all hit the same rate-limited LLM endpoint and retry in synchronized waves (N executions × R retries = N×(R+1) total calls). Guards: SlackEventGuard (per-channel LLM call sliding window), SlackBotLoopGuard (subtype filter + post-rate ceiling), SlackEventIdempotency (event_id dedup with async-acknowledge pattern), and SlackWorkflowCircuitBreaker (opens on 3 consecutive 429s, blocks for 90s).
Retool bills per workflow run and its subworkflow composition lets AI-generated lists create geometric run multiplication. Four failure modes specific to Retool AI Agents and Workflows: subworkflow recursion amplification where a Run Workflow step called per AI-generated subtask creates a two-level fan-out of 421 billed runs from a single trigger, AI query fan-out from listView and table components where an AI query set to auto-run fires independently for each rendered row (200-row listView = 200 AI calls per page load), retry storms where concurrent Retool Workflow runs all hit the same AI API rate limit simultaneously and retry in synchronized waves (20 concurrent runs × 5 retries = 120 total API calls from a single rate-limit event), and database change trigger loops where an AI enrichment workflow triggered by a DB row change writes back to the same table and re-fires itself invisibly. Guards: RetoolWorkflowBudget (depth + total-runs-per-trigger ceiling), RetoolAIQueryGuard (per-session AI query count with listView fan-out detection), RetoolCircuitBreaker (opens on 3 consecutive 429s, blocks for 120s), and RetoolLoopGuard (provenance tag + hop-count ceiling for DB change trigger loops).
Make bills per operation and its Router fires all matching branches simultaneously — unlike Zapier Paths, which routes to exactly one branch. Four failure modes specific to Make AI scenarios: Router branch multiplication where two overlapping filter conditions both match the same AI output (doubling all downstream operations), Iterator fan-out amplification where an AI module returning a variable-length list multiplies downstream operations by list length (a 20-item entity extraction with 4 downstream modules = 81 operations from one run), instant trigger floods where webhook-triggered scenarios fire immediately for every inbound event with no built-in rate limiting (a marketing email blast can exhaust monthly quota in minutes), and Data Store self-trigger loops where AI output written back to a watched Data Store re-fires the same scenario invisibly. Guards: MakeRouterGuard (concurrent branch execution counter), MakeIteratorGuard (array length cap with truncation logging), MakeTriggerGuard (sliding window proxy rate limiter for instant webhook triggers), and MakeDataStoreGuard (provenance tag + hop-count ceiling to break write-back loops).
Zapier bills per task and its retry logic amplifies costs silently. Four failure modes specific to Zapier AI workflows: per-task billing accumulation where each Zapier Agent action counts as a separate billed task (a single support ticket handled by an agent typically burns 6–15 tasks), retry storm amplification where a rate-limited AI step causes Zapier to re-run the entire Zap up to 3 times while re-billing already-completed steps, burst quota exhaustion when a flood of inbound triggers exhausts a month’s task allocation in hours before any monitoring alert fires, and circular inter-Zap trigger chains where an AI Zap writes to a data store that triggers a second Zap that writes back — invisible from the Zapier editor because each Zap only sees its own trigger. Guards: ZapierAgentBudget (per-session action counter + monthly ceiling webhook), ZapierRetryGuard (idempotency check + rate-limit circuit breaker state), ZapierBurstGuard (sliding window session rate limiter), and ZapierLoopGuard (provenance tag + hop-count ceiling).
AWS Strands Agents is Amazon’s open-source Python SDK for building production AI agents on Bedrock, released May 2025. Four cost failure modes specific to its streaming architecture: conversation history compounding that sends the full accumulated context on every turn (total input tokens scale as N²/2, not linearly), tool result injection loops where the model repeatedly calls the same tool on ambiguous results, multi-agent supervisor-worker amplification where each worker agent maintains its own full context window (depth-2 tree with 3 workers per level × 15,000 tokens/session = 135,000 minimum tokens), and Lambda per-millisecond billing drift from idle wait on Bedrock streaming responses that grows with context length. Guards: StrandsTokenBudget, ToolCallGuard with consecutive + total per-tool ceilings, MultiAgentBudget with tree depth and spawn limits, and LambdaSessionGuard using context.get_remaining_time_in_millis() for dynamic wall-clock ceilings.
Flowise and LangFlow wrap LangChain behind a drag-and-drop canvas — which hides four cost failure modes behind approachable node panels. Independent node retries multiply LLM calls exponentially through a chain (a 3-node flow with 3 retries each generates up to 27 calls per failure event). Webhook replay from at-least-once delivery systems (Zapier, Stripe, n8n) retries your $0.20 flow 2–3 times per event when the flow takes longer than 30 seconds to respond. One shared API key for all flows means a single runaway flow exhausts the rate limit for every other flow on the instance simultaneously — triggering a thundering herd cascade when the token bucket refills. Parallel canvas branches fire concurrent tool calls that hammer rate-limited APIs and retry on 429, amplifying the initial burst 3–5×. Guards: FlowBudgetCallback across all nodes, idempotency proxy for webhook endpoints, per-flow API key isolation, and a SemaphoredTool wrapper for canvas tool concurrency.
E2B bills by the CPU-second including idle time between code executions — every second your agent’s LLM is thinking is a billable sandbox second. Four failure modes: sandbox CPU-seconds accumulating during agent think-time (a 15-step agent with 3-second LLM latency accumulates 45 extra billable seconds), execution timeout retry loops that re-run the same expensive computation and compound idle billing, stdout/stderr output accumulation feeding 40k–80k tokens of intermediate results into the LLM context window across a session, and parallel sandbox storms where asyncio.gather() on 20 tasks opens 20 simultaneous billable sandboxes. Guard patterns: scope sandboxes to each execution with the context manager, track timed-out CPU-seconds against budget, strip base64 chart outputs (8k–25k tokens each), and gate concurrency with an asyncio semaphore. Includes E2BCostGuard, OutputBudgetGuard, and ConcurrentSandboxPool in Python.
Groq’s LPU delivers 400+ tokens per second — 5–8× faster than GPU providers. The same loop that takes 30 minutes to burn $5 on GPT-4 completes in under 5 minutes on Groq, before any monitoring alert fires. Rate limit retry cascades grow worse on every retry because each wait cycle lets the agent accumulate more context; the final retry sends 2,000+ extra tokens compared to the first. Groq’s daily TPD cap operates independently of per-minute TPM limits — an agent that carefully respects TPM can exhaust the daily budget by 3 PM UTC and stall for 10 hours. Context accumulation at LPU speed outpaces token counters calibrated on slower providers. Four Groq-specific failure modes with GroqRateLimitGuard, GroqSpeedLoopGuard, GroqDailyBudgetGuard, and GroqContextAccumulationGuard.
JAX traces and compiles one XLA kernel per distinct input shape — an agent that produces variable-length tool results triggers a full 15–180 second recompilation on every unique sequence length. Mixing numpy arrays with JAX GPU tensors inside agent loops causes silent PCIe copies: a 1.8 GB Gemma-7B KV cache copied 20 times per run adds 2–3 seconds of pure transfer overhead invisible to any profiler. Gemma size-switching (2B for short prompts, 27B for complex reasoning) loads both parameter sets into VRAM simultaneously unless the previous model is explicitly evicted, causing OOM or 40–60× CPU fallback on the third model-switch call. Agent frameworks that spawn a new subprocess per request lose JAX's in-process compiled kernel cache entirely — cold subprocess + 27B model = 165 seconds before the first token. Four JAX-specific failure modes with XLARecompileGuard, DevicePlacementGuard, ModelEvictionGuard, and JITCacheGuard.
MLX loads a 7B model in 8–15 seconds per call on M2 hardware; an agent that naively reinstantiates the model per tool call burns 80–150 seconds of serial load time before the first useful token. Metal shader compilation on a cold cache blocks inference for 20–60 seconds on first run — agents spawning fresh Python processes per task pay this penalty on every call. Thermal throttle reduces Apple Silicon clock speeds under sustained inference; agents with latency-based timeouts retry on throttle events and drive the device deeper into throttle in a spiral. KV cache grows at ~0.5 MB per token on unified RAM for non-GQA models; a 32K-context agent session occupies 16 GB of KV cache alone, exhausting memory on a 16 GB M2 before the model weights are counted. Four on-device failure modes with ModelCache, ShaderWarmGuard, ThermalThrottleGuard, and KVCacheGuard.
Microsoft TaskWeaver's Planner-CodeInterpreter architecture loops on persistent code failures: when generated code hits a systemic error the Planner updates its plan and triggers fresh code generation — repeating to the planner.max_steps ceiling without resolving the root cause. The CodeInterpreter injects full execution output (stack traces, stdout from partial execution) into every retry context, growing the per-retry token cost with each attempt. SharedSessionMemory stores every planner turn, code generation, execution result, and response as RoundRecord objects; a 20-round analysis session accumulates 40,000–70,000 tokens injected into every subsequent LLM call. Plugins called inside LLM-generated for-loops make N external API calls per execution block, invisible to any framework-level cost counter. Four failure modes with PlannerRetryGuard, retry output trimming, SessionMemoryGuard, and PluginCallBudget.
Cohere's tool-use loop runs until the model returns finish_reason="COMPLETE" — without a step ceiling, a research agent on a broad query calls tools 20–40 times before surfacing a final answer. The chat_history parameter must be passed in full on every call, and tool results accumulate in that history, growing input costs quadratically with every turn. RAG-mode calls with documents= accumulate injected document context across iterative steps: a 10-step agent that fetches 5 documents per step sends all 50 documents to the final synthesis call. co.rerank() bills per document per query; called inside an agent loop it turns a sub-cent operation into the dominant cost driver for the entire run. Four Cohere-specific failure modes with CohereStepGuard, BoundedChatHistory, DocumentBudget, and RerankGuard circuit breakers.
The Mistral Agents API runs tool call loops until the model decides to terminate — without a step budget, a research agent on a broad task calls tools 30–60 times before surfacing a final answer. Persistent conversation threads inject the full message history into every completion, growing token costs quadratically as the thread ages. Multi-agent handoffs multiply spend: a top-level agent delegating to three sub-agents each running their own loops can generate 10–15× the expected call volume. The built-in code interpreter injects error traces back into the conversation when code fails, creating a retry spiral where each attempt adds a full execution trace overhead. Four Mistral-specific failure modes with Python step budgets, thread token guards, delegation depth limiters, and code interpreter spiral detection.
Griptape agents loop through tool calls using model judgment as the termination condition — without a step cap, a research agent on a broad question calls tools 40–80 times before settling on an answer. ConversationMemory injects the full message buffer into every prompt: a 50-turn session prepends ~15,000 tokens of history overhead before the user's new message appears. RAG-backed tools embed and retrieve on every step inside a running loop, compounding embedding API costs that don't appear in your step counter. Parallel Workflow tasks fan out concurrently without a built-in rate-limit-aware cap, triggering simultaneous 429 errors across all branches. Four Python-specific Griptape failure modes with event-based step guards, buffer memory strategies, a retrieval deduplication cache, and a workflow concurrency semaphore.
Modal's autoscaling model creates four cost failure modes that don't exist on always-on servers. Cold start overhead amplifies when many containers boot simultaneously — a fan-out of 20 parallel sub-agent calls on an all-cold pool pays 20× the GPU reservation overhead during boot. Retry loops at the caller drive queue depth up, causing the autoscaler to provision many containers that all hit the same failure — a cascade that bills for containers that do no useful work. Modal's built-in retries=N composes multiplicatively with framework retries (LangChain, tenacity), generating up to 16 billable GPU runs from a single logical tool call. Short-lived sub-calls (embeddings, classification, token counts) each incur the minimum billing interval per invocation — 200 calls × 80ms of actual compute can cost as much as 200 full-interval container runs. Four guards with a composite ModalCostPolicy.
LiteLLM's num_retries and fallbacks compose multiplicatively — 3 retries × 3 fallback providers means a single failed request generates up to 10 LLM calls before surfacing an error. Latency-based routing shifts traffic to the "fastest" provider during slow periods, which then slows under concentrated load and triggers more retries — a cascade that runs at 3–5× normal volume. Streaming responses from several providers omit the usage field, silently bypassing max_budget enforcement. Misconfigured proxy-in-proxy aliases create recursive call loops that exhaust budget in seconds. Four proxy-layer failure modes with Python circuit breakers and a composite LiteLLMCostPolicy.
Mastra agents running without maxSteps loop through tool calls until they hit a context window limit or timeout — a broad research question can trigger 40–80 tool iterations. Workflow steps with retryConfig multiply LLM call costs on transient failures: 3 steps × 3 retries = 12 LLM calls for what should have been 3. Mastra's Memory system retrieves semantically similar history before every new LLM call, and injected context grows proportionally with conversation length. Parallel fan-out steps without a concurrency cap multiply costs by the number of runtime branches. Four TypeScript failure modes with circuit breaker guards and a composite MastraCostPolicy.
LlamaIndex's retrieve-synthesize architecture stacks retrieval costs on top of LLM synthesis costs at every step. Workflow Context objects accumulate all intermediate chunks and summaries, re-sending them on every subsequent synthesis call. SubQuestionQueryEngine generates LLM-determined sub-question counts — complex queries routinely produce 15–25 parallel retrieve-and-synthesize cycles. ReActAgent iterates retrieval with reworded queries until max_iterations hits. QueryPipeline validation loops cycle through expensive pipelines on insufficient grades. Four failure modes with Python circuit breakers and a composite LlamaIndexPolicy.
CrewAI hierarchical crew orchestration stacks manager LLM planning calls at every level — a three-tier crews-of-crews hierarchy triggers seven manager LLM planning calls before the first task runs. Cross-crew shared memory accumulates all sub-crew outputs into a common retrieval pool. kickoff_async() fan-out from LLM-generated task lists spawns unbounded parallel crews. Hierarchical delegation retries multiply across three independent retry layers for up to 27× cost on a single failing task. Complete Python guards and a composite CrewsOfCrewsPolicy.
The Gemini Live API maintains a persistent WebSocket session with a rolling in-session context window — every audio turn, tool call, and function response appends tokens billed at text rates on top of per-second audio streaming. Background noise triggers barge-in loops that waste partial generation output. Tool call spirals inside a single turn chain 5–10 calls on a failed lookup. Reconnecting after the 15-minute limit re-pays the full accumulated context. Four failure modes with Python circuit breakers and a composite GeminiLivePolicy.
Temporal persists every activity result — including full LLM responses — as immutable history events. A research agent running 200 LLM activities generates 600+ history events and megabytes of serialized output, forcing full replay on every signal. Unlimited MaximumAttempts on LLM activities multiplies Temporal Cloud action billing. LLM-seeded child workflow fan-out spawns unbounded concurrent executions. ContinueAsNew neglect causes 400MB replay overhead per signal. Four failure modes with Go and Python circuit breakers and a composite TemporalAgentPolicy.
Thread history accumulation sends input token costs up 214× on a 200-turn thread. Unclassified run polling retries pay full context cost on every failed run. Uncapped tool call steps inside a single run multiply tokens 10×. Per-message file attachments re-embed the same files on every turn. Four Assistants API failure modes with complete Python circuit breakers and a composite AssistantGuard class.
with_structured_output() Pydantic validation retry loops, OutputParserException cascade via RetryWithErrorOutputParser, bind_tools() agent executor spirals, and custom @validator infinite re-calls — four hidden cost multipliers in LangChain's structured output stack with complete Python circuit breakers.
Dapr's virtual actor re-entrancy, persistent reminders, durable workflow retry policies, and external state store accumulation create four AI cost failure modes that survive process crashes — invisible to in-process guards. Complete Python circuit breakers for Dapr AI orchestration with a composite DaprAgentGuard class.
CrewAI Flows' @listen and @router decorators create event-driven cycles that basic max_iter guards miss entirely — listener chain cycles, router infinite re-routing, parallel listener fan-out, and FlowState accumulation. Four failure modes with complete Python circuit breakers and a GuardedFlow base class.
RunnableRetry exponential blowouts, ConversationBufferMemory explosions, RunnableParallel fan-out cost multiplication, and unbounded streaming accumulation — four LCEL-specific failure modes that LangGraph guidance won't catch. Complete Python guards including a BudgetCallbackHandler and RetryBudget wrapper.
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 44 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.
