When 41 of 20,000 Message Batches Requests Quietly Vanished — Field Notes on Reconciling and Requeuing Partial Failures
processing_status: ended does not mean every request succeeded. How errored and expired results hide inside a finished batch, and how a custom_id ledger catches every gap and requeues safely — with real cost and timing numbers.
When the Model Survives but One Parameter Expires: A Dated Deprecation Calendar for Claude API Requests
Your model ID can stay valid while a parameter you pinned quietly reaches its sunset date and takes the batch down with it. Here is a design that breaks a request into parts, gives each part its own expiry date, and catches the problem before the call goes out — with working TypeScript and real operational numbers.
When Claude API Document Extraction Is Confidently Wrong — Field Notes on Catching Silent Errors with Invariants
In structured extraction from invoices and contracts, the real danger isn't a crash — it's a value that's silently wrong while the schema validates and confidence reads high. Field notes on invariants, two-pass extraction, and tracking field-level error rates.
A Fail-Closed Model Pricing Registry So New Models Don't Quietly Break Your Cost Math
When Opus 4.8 and Haiku 4.5 landed in the Messages API, rates scattered across my code silently skewed the cost rollup. Here is how to centralize per-model rates and fail closed on unknown models, with complete working code.
The Same 429 Wears a Different Face on Each Route: Running Claude Safely over Anthropic Direct and Azure Foundry
With Claude now generally available on Microsoft Foundry, a two-route setup is realistic even for solo developers. Here is how to fold the route-by-route differences in 429s and retry-after into one normalized error type and a single backoff policy.
When a Tool Result Is Too Big and Melts Your Context Window: Designing Cursor-Based Pagination
When a list tool returns hundreds of rows at once, an agent's context can collapse in a single call. Here is a cursor-based pagination design that keeps tool output small and protects your token budget, with working code.
Stop rebuilding intermediate files every request: reuse the Code Execution container to carry pipeline state
How to reuse the Code Execution container across requests by passing its container ID, so generated files and intermediate results carry over to the next step. Includes the execution-time billing trap and how to handle container_expired safely, with working code.
Let Claude Actually See the Images Your Tools Return — Use Image Blocks in tool_result and Cut Tokens by Roughly 10x
Stuffing a base64 string into a tool_result makes the same image cost roughly 10–20x more tokens. Here is how to return it as an image content block instead, with SDK code, a token-cost estimate, and the gotchas I hit in production.
Did That Post Actually Go Through? Safely Retrying an Interrupted MCP Write Without Double-Executing
When an MCP write tool call is interrupted by a dropped connection, you can't tell whether the server ran it. Here's why naive retries cause double-execution, and a working wrapper that uses idempotency keys and a reconcile read to retry safely — with examples from an unattended pipeline.
Measure Streaming CPU and Dropped Chunks to Stabilize Long Batch Jobs
You start an overnight batch, and by morning only half of it finished. The culprits were CPU pinned during streaming and a quiet connection drop. Here is a monitor wrapper that measures stream CPU and throughput, and resumes from interruptions.
Designing the Give-Up Condition in Self-Repair Loops: Four Error Classes, Four Retry Budgets
LLM self-repair loops break on the fantasy that 'if you keep fixing, it eventually passes.' Classify errors into four classes, give each its own retry budget. Working TypeScript and real cost numbers included.
When the Same Model Name Starts Behaving Differently: A Startup Canary for Unattended Pipelines
An in-place Opus upgrade can change your output, and an unattended publishing pipeline will never notice. Here is a lightweight startup canary that fingerprints behavior, catches drift, and halts the batch — with measured cost and latency.