Building an Enterprise AI Agent Platform in Go
This series documents what we learned building a production AI agent runtime and Aiden — StackGen’s multi-tenant orchestration platform for enterprise SRE and platform teams. Every post is grounded in shipped behavior and production failures, not demo polish.
Start with a pack
| Pack | For |
|---|---|
| Go agent runtime | Runtime definition, Go vs Python, platform split |
| SRE on-call | Triage, RCA, observability |
| Evidence-gated RCA checklist | Operator review of agent write-ups |
| “Done” checklist | When not to trust agent completion |
Topic hubs
Dive by theme:
- AI agent workflows — multi-stage pipelines, bring-up, evidence-gated RCA
- AI agents for SRE — incident triage, observability, tokenomics
- Go AI agents — language choice, platform architecture, IaC config
- AI agent runtime — loop vs platform
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Start here
Python vs Go for AI Agents — Why We Chose Go
Go vs Python for AI agents (golang vs python for ai) — production agent runtime, concurrency, deployment, a...
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Then read
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.
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Then read
Go Platform Architecture at Speed — Without Drowning
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Every post, month by month
40 posts across 3 months.
August 2026 11 posts
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Why hallucination guards fail when they truncate evidence, how claim packing works, and the pitfalls of fail-open and brittle overlap.
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Single-agent vs multi-agent orchestration for SRE triage — a fair A/B, what each shape is good at, and a decision framework without defaulting to either.
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Adopt Aiden board SDLC with one OpenTofu module — Specify → Research → Plan → review PR — without wiring every sg_* resource by hand.
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Wire Aiden board SDLC the hard way: every sg_* resource from provider and models through GitHub integration, agent, workflow, webhook, and status poll.
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What SRE AI agents are — AI for incident triage, diagnostics, and RCA with bounded autonomy — vs chatbots and open-ended remediation demos.
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What an AI agent runtime is — the production loop for planning, tools, and context — vs a GenAI platform, chatbot, or notebook demo.
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SRE for agentic systems and AI agents. Introducing Judgment SLOs and how to measure agentic drift in production.
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PII redaction for AI agents needs two views: protected model history and authorized operator visibility when debugging tool calls.
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How to steer an AI agent mid-run: additive feedback, iteration-boundary delivery, and completion gates so corrections change the active task.
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Prompt caching for AI agents fails when context is turbulent — stable prefixes, early compaction, isolated helpers, and references beat payload copies.
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AI agent loop detection can erase good work — preserve the best evidence-backed answer when a stalled run ends instead of a generic failure.
July 2026 17 posts
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AI agent root cause analysis fails when digs find a lead and discard it — peer noise, wrong fire-time windows, and transcript gates for AI SRE agents.
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When an AI SRE agent hits a dead end during incident response, creativity means searching harder — not hallucinating rule IDs, metrics, or a tidy root cause analysis.
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AI agent root cause analysis for SRE: soft prompts don't stop bad RCAs — curiosity checklists, hard gates, and batched validation before confident narratives.
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Why production AI agents need an independent completion check — options we rejected, the goal-scoped loop we shipped, and the papers that shaped it.
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A production lesson in agent observability: measure telemetry quality before trusting reliability, correctness, cost, or latency scores.
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Hypothesis-driven debugging for AI SRE root cause analysis — climb identity and onset before deploy theories, keep parallel branches, prove first and narrate last.
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Production-ready AI agents need receipts, not fluent demos — evidence gates, bring-up discipline, HITL tiers, and eval checklists for enterprise agent pilots.
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AI agent learning loop for production — daily digests become human-approved workflow and policy changes, not bigger vector stores. HITL governance for enterprise agents.
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Wiki playbooks trained a generation of on-call engineers. AI agents need procedures that change with your stack — and that's an argument for version-controlled runbooks, not against human-friendly docs.
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Runbooks are fine for humans and stable ladders — bad as the agent's only navigation. After months of multi-plane RCA: topology, gates, and verify-first beat another forty-page notebook.
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The best way to debug a multi-step AI agent and execution logs. Bring up multi-stage agent pipelines one stage at a time.
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LLM token budget strategies for production agents — context budgets, tool compression, and FinOps loops that keep sessions finishing.
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Evidence-based verification for AI agents — pull proof from ArgoCD, Datadog, and systems of record; let Go own pass/fail.
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Evidence-gated multi-plane RCA — fixed DAG, structural evals, and token-aware tool loops for production agent workflows.
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AI incident triage for SREs — what actually helps on-call versus demo theater. Canonical copy now on StackGen.
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Production AI agent tool calls need layered JSON repair — why one pass fails and what we learned in Go middleware.
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We built a proprietary product. We also merged 17 PRs into the agent framework we depend on. Here's how to navigate that tension.
June 2026 12 posts
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AI agent runtime vs multi-tenant platform for GenAI — why we split the Go runtime from enterprise orchestration (Aiden).
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We use Terraform to configure our AI agents. Not YAML. Not a dashboard. Terraform. Here's why.
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Observability for AI agents in production: session traces, tool attribution, token budgets, audit trails, and signals traditional APM misses.
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Secure agent workflows — defense-in-depth for AI agents with real tools: policy, HITL, and layered governance in production.
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Human-in-the-loop is supposed to make agents safer. It can also make them useless. Here's how to find the balance.
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Agent skill distillation without fine-tuning — teach agents from production traces, not distilled models. Reusable skills without GPU fine-tuning.
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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.
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What happens when you take an arXiv algorithm to production. The best way to debug a multi-step AI agent and avoid these 6 ReAcTree bugs.
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Go platform architecture for a production AI agent codebase — patterns that kept rapid development sustainable.
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We tried YAML, considered PKL, and landed on TOML for agent configuration. The reason surprised us.
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Go vs Python for AI agents (golang vs python for ai) — production agent runtime, concurrency, deployment, and when Python wins for GenAI.
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LLM performance metrics — TTFT, time per output token (TPOT), and token-era equivalents of web vitals for agent workloads.
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Posts outside the numbered series (e.g. cloud entitlements, web→LLM metrics) live on the homepage archive.
Stay in the loop — production notes on AI agents, workflows, and SRE.
Low volume — new posts and curated reading lists. Unsubscribe anytime.
FAQ
Why build an enterprise AI agent platform in Go?
Go gives you static typing, simple deployment, and concurrency primitives that map cleanly to multi-stage agent workflows. This series covers when that trade-off beats Python-first AI frameworks in production.
Where should I start reading?
Use a starter pack: Go agent runtime (definition, why Go, platform split) or SRE on-call (triage, RCA, observability). Then open the searchable series archive and follow series_order. Each post is self-contained but builds on prior lessons.
Who is this series for?
Staff engineers, platform teams, and SREs shipping agentic workflows to production — not tutorial readers looking for a hello-world chatbot.