Stay informed with the latest product releases, launch announcements, and market insights from the SmaugBrain team.
Learn how to secure AI agent credentials with short-lived tokens, least privilege, approval gates, secret isolation, audit logs, and tested revocation.
Learn how to define AI agent SLOs for correct completion, tail latency, tool reliability, human intervention, and cost per successful outcome.
A practical guide to AI agent cost control using request budgets, token limits, tool quotas, workflow guardrails, monitoring, and approval gates.
Understand the difference between an AI agent and an agentic workflow, then use a practical decision framework to choose the right design, controls, and approval model.
A practical guide to AI agent approval workflows, scoped permissions, immutable requests, audit trails, and human review for high-impact automation.
Discover how to build reliable AI agent workflows with exponential backoff, circuit breakers, and idempotency keys. Practical retry strategies for production cloud automation.
Learn practical strategies to reduce AI agent token consumption by managing memory effectively. From context trimming to persistent memory, reduce costs by up to 80%.
AI agents running automated workflows will eventually hit errors. Learn five practical error-handling strategies: retry with backoff, fallback actions, circuit breakers, compensating rollbacks, and human escalation.
A practical guide to securing MCP servers for cloud AI agents with least privilege, policy gates, sandboxing, approvals, and audit logs.
Duplicate webhook callbacks can produce duplicate messages, tickets, and business writes. This article explains practical implementation methods for idempotency keys, unique constraints, deduplication windows, external operation receipts, compensation, and dead-letter queues.
When an AI Agent can read files but cannot upload them, the cause usually involves path isolation, write permissions, format, size, web file controls, or attachment association. Use these 7 checkpoints to locate the failure, then verify the upload result by rereading the final record.
Should an AI Agent execute directly or generate a draft first? This article explains how to choose among three levels—draft, execution after approval, and automatic execution—based on reversibility, the risks of repeated execution, acceptance difficulty, and the conditions for human takeover.
Does an AI Agent scheduled task show success without sending a notification? Troubleshoot empty output, delivery destinations, platform permissions, attachments, timeouts, rate limiting, and retries in sequence, then verify the fix with an actual scheduled trigger.
When WordPress internal links return 404 errors, how do you identify their sources, confirm the correct targets, repair the content, and complete front-end verification? This workflow can also be adapted into a scheduled checking task.
When an AI agent fails, checking the API status alone is not enough. This article provides checklists for trace events, five categories of metrics, quality evaluation, alert response, and privacy-conscious logging.
AI agent hallucinations cannot be solved with a single prompt. This article establishes layered defenses covering input, evidence, output validation, tool verification, and human review.
RPA is suitable for stable rules and UI operations, while AI agents are better suited to understanding unstructured information and making dynamic judgments. This article provides selection questions, a hybrid architecture, and a migration sequence.
Install the SmaugBrain Windows desktop app using the current official download page: confirm the version, fully extract the files, launch the app and log in, and troubleshoot issues with opening the app or unavailable Agents.
Before launching NL2SQL, you must address schema mapping, ambiguity, read-only permissions, query limits, and auditing. This article provides a secure workflow from question to execution.
When RAG answers miss the point, do not switch models first. This article provides a layer-by-layer troubleshooting process covering documents, chunking, queries, retrieval, ranking, and generation, along with a repeatable evaluation method.
When building your first AI agent, do not aim for an all-purpose assistant immediately. This article uses a five-step method covering the goal, input, tools, boundaries, and acceptance criteria to help you implement a small, testable use case.
An AI agent cannot be evaluated based on a single demonstration. This article explains task success, output quality, efficiency, and cost metrics, and provides a process for test sets and regression acceptance testing.
When AI agents are connected to databases, email, and external tools, their security boundaries expand accordingly. This article provides a pre-launch checklist covering prompt injection, least privilege, privacy, and auditing.
How can AI agent skills be both reusable and secure? This article covers interfaces, registration, execution, error handling, and testing, providing a checklist that can be used directly in design reviews.
AI agent costs come from more than just tokens. Starting with per-task accounting, model routing, context, tool calls, storage, and failure retries, this article presents a six-step approach that prioritizes measurement before optimization.
Multi-model routing is not about automatically selecting the “most capable model.” This article explains how to establish task tiers, model capability cards, quality thresholds, fallback chains, and continuous evaluation to prevent quality from spiraling out of control after costs are reduced.
AI agent-assisted development cannot stop at code generation. This article presents a complete development workflow covering requirement constraints, minimal changes, code review, automated testing, and human-approved merging.
An AI Agent knowledge base is not built by simply uploading every file. This article provides an actionable process covering question scope, material cleanup, chunking, metadata, permissions, test sets, and update responsibilities.
Before connecting an AI Agent to a CRM, email, code repository, or database, first define the read and write boundaries. This article compares APIs, webhooks, and skill wrappers, and provides a checklist covering permissions, idempotency, error handling, and acceptance testing.
Content marketing automation should not bypass editorial review. This article explains how to connect topic selection, source verification, initial drafting, brand review, channel adaptation, publication confirmation, and performance analysis into a controlled workflow.
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