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A moderation classifier (GPT-4o zero-shot) hits 96% accuracy on a balanced test set but the remaining 4% is concentrated on borderline cases — which is exactly the population humans most want right. False negative rate on borderline-harmful content is ~18%.
A text-to-SQL agent generates queries that run but ignore obvious indexes — doing full scans on the 200M-row events table when a user-id index would answer the query in <50ms. Showing the schema DDL (including indexes) in the prompt helps marginally.
Our production eval dataset (derived from real user queries, refreshed monthly) has enough drift that our fine-tuned model is consistently 2-3 points behind on "new" eval slices. By the time we retrain, the drift has moved again.
pgvector HNSW index on a 10M-row chunk table takes 800ms p95 for top-10 nearest-neighbor search. Index size is 14GB (larger than the data). Rebuilding with ef_construction=64 and M=16 didn't help. Queries should be ~50ms at this scale.
A planning-executor agent sometimes calls tools speculatively — e.g., generates a document draft early while still gathering requirements. When requirements change mid-task the speculative work is wasted, costing time and compute. The agent doesn't cancel or revise the speculation.
When an agent escalates to a human support rep, the rep sees the conversation transcript but nothing about the agent's internal state (what tools it tried, what it concluded, what the user already confirmed). Rep has to re-read everything and often asks questions the user already answered.
A research-assistant agent cites sources inline with [1], [2], etc. About 8% of citation indices don't match the retrieved source list — either off-by-one or pointing to a source that wasn't retrieved for that claim.
A cron-scheduled daily agent (runs at 7am local time via Vercel Cron) misses one run twice a year on DST transitions. Weeks 2/3 of March and November have either a duplicate run or a missing run.
A Supabase query behind a row-level security policy takes 30+ seconds for a signed-in user. Without RLS the same query runs in 40ms. EXPLAIN shows the policy's USING clause forces a sequential scan over 2M rows per call.
A downstream customer's agent hammers our GraphQL API with unpaginated list queries, retrieving 50k records per request. Rate limiting on requests-per-second doesn't cap this because the agent's request rate is low — it's the response size that's the problem.
When streaming, the client tries to detect whether the model is producing a tool call vs. a regular text response by watching for the tool-call marker. Sometimes the marker arrives split across two tokens and the client's regex misses it, rendering a broken UI state.
Adding a "reflect and improve" step to an agent's output (agent produces, critiques, revises) degrades quality on our eval by ~4 points. The critique identifies real issues, but the revision introduces new ones or softens correct claims.
A booking agent misfires about 20% of the time — either booking when the user was just exploring, or failing to book when the user clearly said "go ahead". Intent classification model (fine-tuned distilbert) labels at 88% accuracy in isolation but the errors compound in-context.
An agent with user-isolated memory stores each user's context under a user-id key. Under load, some memory reads return another user's data. Suspect a cache-key or connection-pool bug, not a product-design flaw — the schema enforces isolation at write.
A scraping agent rotates through a pool of 200 residential IPs (Bright Data) and still gets blocked by a specific target site within ~3 hours. The block appears to be account-level or browser-fingerprint-level, not IP-level.
ElevenLabs voice clone works well on neutral sentences but fails on emotionally-charged utterances — "I'm so sorry for your loss" sounds flat and slightly wrong. Adding SSML <prosody> tags helps slightly. Using the Turbo model helps neither.
Two A2A-protocol agents (an editor and a fact-checker) both modify a shared document. Without coordination they produce conflicting edits (the editor rewrites a sentence the fact-checker flagged, losing the flag; the fact-checker later re-flags, starting a loop). Naive mutex doesn't work because both agents need concurrent read+write.
Streaming structured output: client gets tokens as they arrive and tries to parse partial JSON progressively for UI updates (showing fields as they complete). 15-20% of streams produce unparseable intermediate states even though the final stream is valid. Current approach (trying JSON.parse on every token) fails on every partial stream.
A browser automation agent can log in to Salesforce / HubSpot / Notion and navigate UI reliably. But completing multi-step flows ("move this opportunity to 'Closed Won', then create a follow-up task for next Tuesday") fails ~60% of the time because selectors shift between steps or state from step N isn't available at step N+1.
Observability for a production agent is limited to (a) LLM request/response pairs, (b) tool call inputs/outputs. When a user reports "the agent did the wrong thing", reconstructing why requires manually tracing through dozens of LLM calls. Tried LangSmith, Helicone, and custom OpenTelemetry — all capture data, none structure it usefully.
A reward model trained on ~40k preference pairs consistently rates longer responses higher, even when content is wrong. Correlation between reward score and response length is 0.71 on a held-out set. Suspect the annotators (expert contractors) preferred verbose answers.
A code-generating agent writes implementations AND tests. Generated tests pass. Human review catches off-by-one errors in the implementation that are masked by the generated tests (tests have the same bug). This defeats self-test as a quality signal.
OpenAI's structured outputs mode returns valid JSON that matches the schema syntactically but picks the first enum value regardless of input when the enum has >20 values and is nullable. Reducing the enum or making it non-nullable fixes it. Reproduced on gpt-4o and gpt-4o-mini.
A real-time voice agent (Deepgram STT → gpt-4o → ElevenLabs TTS) has p95 latency of ~900ms but p99 of 4100ms. The p99 spikes are unpredictable and make conversation feel broken. They don't correlate with query complexity.
An autonomous research agent running multi-hour tasks (ingest papers, synthesize, write a report) hits the 200k Claude context window around hour 2 and then either truncates crucial early context or crashes the planning loop. Summarization-as-you-go reduces fidelity of the synthesis.
A ChromaDB-backed agent memory persists "User's preferred programming language is Python" but the user has since said "I've switched to Rust". The agent still retrieves and acts on the Python fact because it has higher embedding similarity to Python-framed queries. Overwriting isn't happening because each statement becomes a new vector.
Fine-tuning Llama 3.1 70B with QLoRA on ~50k domain-specific examples shows training loss decreasing nicely but instruction-following on out-of-domain tasks collapses around step 800. Model starts ignoring system prompts, hallucinating JSON keys, and outputting domain-specific tokens in unrelated contexts.
Internal estimates projected ~$800/mo for a 1,000-user beta of an agent-powered coding assistant. Actual month 1 was $8,900. OpenAI usage dashboard shows the spike is concentrated in gpt-4o completion tokens, not input. Mean conversation length is 12 turns.
An MCP server (stdio transport) works flawlessly when configured in Claude Desktop but times out or returns nothing when invoked from a custom agent using @anthropic-ai/sdk's tool_use interface. No error logs on either side. The server process starts, but no tool call ever arrives.
An LLM-as-judge eval pipeline (gpt-4o as judge, rubric-based) consistently scores agent outputs higher than human reviewers. The gap is ~1.3 points on a 5-point scale. Swapping judge models (Claude, Gemini) narrows the gap but doesn't close it. The issue blocks us from trusting the eval for regression detection.
A Claude Sonnet 4.5 agent loops: calls search_api("foo") → gets 429 rate limit error → calls search_api("foo") again → 429 → repeats 6-8 times until the outer loop kills it. Putting "do not retry the same call" in the system prompt does not reliably prevent it.
A CrewAI crew of 5 specialist agents (researcher, writer, editor, fact-checker, SEO) duplicates work: the researcher produces a draft, the writer re-researches, the fact-checker re-fetches sources already fetched. Shared memory is configured but seemingly ignored.
A LangGraph agent using the interrupt() pattern for human approval gates restores from checkpoint but loses the interrupt context, so on resume it replays the last completed step instead of the pending-approval step. Redis checkpointer. Using LangGraph 0.2.x.
An autonomous browsing agent using Playwright + Chrome gets blocked by Cloudflare Turnstile challenges on about a third of target sites. Residential proxies reduce the rate but don't eliminate it. The agent cannot progress past the challenge without human hand-off, which defeats the use case.
An OpenAI gpt-4o agent running a 15-turn customer support conversation starts omitting tool calls from its output around turn 6-8 even when the user asks for an action that requires a tool. The assistant produces a plausible text answer instead. Temperature=0, full tool schema in every request, system prompt re-asserts the tool-calling contract.
A retrieval pipeline keyed on OpenAI text-embedding-3-large returns confidently wrong chunks when the user query names a section or chapter ("summarize section 4.2"). The retriever ranks semantically similar content higher than the exact section match. Rewriting the query, reranking with a cross-encoder, and adding a small keyword boost all help partially but none reliably beat ~75% exact-match accuracy on section-by-number queries.