Tag: ai-agents

Posts tagged ai-agents:

• How to Debug Multi-Stage AI Agent Workflows — Bring Up Like Hardware — Debug multi-stage AI agent workflows by bringing up one stage at a time against golden gates — plus why scoring tool calls beats grading transcripts.

• LLM Tokenomics for Production Agents — Context Budgets as an Operating Model — LLM tokenomics for production AI agents — context budgets, tool compression, and FinOps loops that keep sessions finishing.

• Evidence-Based Verification — Don’t Trust Self-Report, Check the System — Evidence-based verification for AI agents — pull proof from ArgoCD, Datadog, and systems of record; let Go own pass/fail.

• Evidence-Gated RCA — Prove, Then Narrate — Evidence-gated multi-plane RCA — fixed DAG, structural evals, and token-aware tool loops for production agent workflows.

• AI Incident Triage for SREs — What Actually Helps On-Call — AI incident triage for SREs — what actually helps on-call versus demo theater, grounded in parallel context gathering in Go.

• Why One JSON Repair Pass Isn’t Enough for Production Agent Tool Calls — Production AI agent tool calls need layered JSON repair — why one pass fails and what we learned in Go middleware.

• Contributing Back While Building a Commercial Product — We built a proprietary product. We also merged 17 PRs into the agent framework we depend on. Here’s how to navigate that tension.

• AI Agent Runtime vs Platform — Why We Split Them — AI agent runtime vs multi-tenant platform architecture in Go — why we split the CLI loop from Aiden.

• Terraform for Agent Configuration — Infrastructure as Code Meets AI Governance — We use Terraform to configure our AI agents. Not YAML. Not a dashboard. Terraform. Here’s why.

• You Can’t Debug What You Can’t See — Observability for AI Agents — Observability for production AI agents — session traces, tool attribution, and token budgets beyond traditional APM.

• Your Agent Has Root — Defense-in-Depth for AI Agents That Wield Real Tools — Your agent can run rm -rf /. Your prompt saying ‘don’t do that’ is not security. Here’s why one layer is never enough.

• The HITL Paradox — When Human Approval Makes Agents Worse — Human-in-the-loop is supposed to make agents safer. It can also make them useless. Here’s how to find the balance.

• Agent Skill Distillation Without Fine-Tuning — Agent skill distillation from production traces — teach agents new runbooks without fine-tuning.

• Pensieve — Memory Management for AI Agents That Actually Forget — Your agent remembers everything. That’s a bug, not a feature. Here’s how we built a memory system that learns, forgets, and self-prunes.

• Implementing ReAcTree — 6 Production Bugs the Paper Didn’t Warn You About — What happens when you take an arXiv algorithm to production. We found 6 bugs that no paper mentions.

• Go Platform Architecture at Speed — Without Drowning — Go platform architecture for a production AI agent codebase — patterns that kept rapid development sustainable.

• TOML Over YAML and PKL — How We Stopped Fighting Config and Started Shipping — We tried YAML, considered PKL, and landed on TOML for agent configuration. The reason surprised us.

• Go vs Python for AI Agents — Why We Chose Go — Go vs Python for production AI agents — concurrency, deployment, and when you shouldn’t follow our path.

• LLM Performance Metrics — From Lighthouse to the Token Era — LLM performance metrics: the token-era equivalent of FCP, LCP, and TBT — and how to debug them.

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