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
Suggested for you
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Start here
Python vs Go for AI Agents — Why We Chose Go
Go vs Python for AI agents: why we chose Go for a production agent runtime — concurrency, single-binary ops...
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Then read
TOML Over YAML and PKL — How We Stopped Fighting Config and Started Shipping
TOML vs YAML vs PKL for agent configuration: why we stopped fighting YAML, skipped PKL, and shipped on TOML.
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Then read
Go Platform Architecture at Speed — Without Drowning
Go platform architecture for a production AI agent codebase — patterns that keep rapid development sustaina...
Every post, month by month
51 posts across 3 months.
August 2026 23 posts
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Simple vs plan on AppWorld skills smoke: plan wins some tasks, ties on others, and always costs more. Choose the mode that fits the job — both belong in the toolkit.
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Single-agent MCP loaded 453 tools (~17k peak). A planner peaked lower, spent ~2× total tokens, and sometimes won quality. Report pass@1 and pass@3 — that is not pass^k.
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Unattended AI agent evaluation fails when the model asks the user or reports done with zero MCP calls. Put those rules in the harness, not the framework.
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Run AppWorld locally with the Aiden agent runtime: docker-compose, MCP HTTP wiring, health gates, and lessons from replacing custom shims with the official three-server stack.
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Stop duplicate agent workers: a handoff gate fixed fairness 5/5 and cut planner token tax on AppWorld evals — partial pass% rose while strict TGC remains a separate harness goal.
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AI agent eval failure modes on AppWorld: budget stops before evaluate, PII placeholders poison tool calls, 422 wrong methods, and prose PASS vs judge FAIL.
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Agent orchestration tax after a fair eval: on AppWorld tasks, planner paths cost ~1.6× tokens and ~3× iterations — measure coordination cost separately from benchmark success.
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Fair agent evals for planner vs single-agent: match domain tool access on workers before you compare tokens, pass rate, or declare a routing winner.
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From vibes to contracts: how we rebuilt agent evals around eval sets, rubrics vs criteria, a grader stack, and pass^k reliability.
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Canary-first evals for live SRE investigate: check the canary before burning judge tokens, and never treat a draft RCA as done.
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AI SRE agent benchmarks: wall time, tool calls, tokens, and ReAcTree tax — fair A/B numbers so you know when orchestration is worth the cost.
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Reasoning effort is not a free upgrade for tool-heavy AI agents. Live SRE A/Bs: blanket high timed out, adaptive low→high dug deeper.
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Claim-aware evidence packing: don't accuse AI agents of inventing what you truncated. Why hallucination guards fail when they drop the receipts.
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Single-agent vs multi-agent for SRE triage: a fair A/B, what each shape wins at, and a decision framework so you stop defaulting to either.
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Aiden the easy way: one OpenTofu module from a vague GitHub issue to a review PR — Specify, Research, Plan, without wiring every resource by hand.
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Aiden the hard way: turn a vague GitHub issue into a review PR — GitHub integration, agent, workflow, webhook, and status poll wired by hand.
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What are SRE AI agents? AI for incident triage, diagnostics, and RCA with bounded autonomy — not a chatbot and not open-ended remediation demos.
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What is an AI agent runtime? The production loop that plans, calls tools, and manages context — not a GenAI platform, chatbot, or notebook demo.
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SRE for agentic systems: uptime isn't enough. Judgment SLOs and how to measure agentic drift when the agent can be 'up' and still wrong.
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PII redaction for AI agents needs two views: a protected model history and authorized operator visibility when debugging tool calls.
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Can you interrupt or redirect an AI agent mid-response? Yes. How to send steer or cancel signals mid-stream and change the active task without restarting.
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Prompt caching for AI agents is an architecture problem: stable prefixes, early compaction, and references beat copying a turbulent payload.
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AI agent loop detection can erase a good answer. Preserve the best evidence-backed result when a stalled run ends — don't throw the work away.
July 2026 16 posts
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AI agent root cause analysis fails when a dig finds a lead and discards it — peer noise, wrong fire-time windows, and transcript gates for AI SRE.
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When an AI SRE agent hits a dead end, be creative — don't invent. Search harder instead of hallucinating rule IDs, metrics, or a tidy RCA.
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AI agent root cause analysis for SRE: curiosity before confidence. Soft prompts don't stop bad RCAs — checklists, hard gates, and batched validation do.
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Is the AI agent task actually done? Why production agents need an independent completion check — not a self-graded 'I'm finished.'
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When your AI agent scorecard lies: measure telemetry quality before you trust reliability, correctness, cost, or latency scores.
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Hypothesis-driven AI SRE root cause analysis: climb identity and onset before deploy theories, keep parallel branches, prove first and narrate last.
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From demo to deploy: production-ready AI agents need receipts, not fluent demos — evidence gates, HITL tiers, and eval checklists for enterprise pilots.
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AI agent learning loop: daily digests become human-approved workflow and policy changes — not a bigger vector store.
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Beyond Confluence runbooks: why GitOps triage steps matter for AI agents — version-controlled procedures that change with your stack.
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Your RCA agent doesn't need another runbook — it needs a map. Topology, gates, and verify-first navigation beat a forty-page notebook.
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LLM tokenomics for production agents: token budget strategies, context compression, and FinOps loops that keep sessions finishing.
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Evidence-based verification for AI agents: don't trust self-report — pull proof from ArgoCD, Datadog, and systems of record, then let Go own pass/fail.
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Evidence-gated RCA for AI SRE agents: prove with receipts, then narrate. Fixed stages, structural evals, and token-aware tool loops.
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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 agent tool calls need layered JSON repair — why one pass fails on malformed LLM output and what we learned in Go middleware.
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Contributing back while building a commercial product: how we shipped a proprietary platform and still merged PRs into the agent framework we depend on.
June 2026 12 posts
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AI agent runtime vs platform: why we split the Go runtime from Aiden, a multi-tenant orchestration layer for enterprise GenAI agents.
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Terraform for AI agent configuration — why we use infrastructure as code, not YAML dashboards, to govern production agents.
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Observability for AI agents: session traces, tool attribution, token budgets, and audit trails — the signals traditional APM misses in production.
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Defense-in-depth for production AI agents: layered policy, HITL, and tool governance when the agent has root — prompts are not security.
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HITL approvals can make AI agents worse. How to find the human-in-the-loop balance so review gates protect production without stalling the agent.
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Agent skill distillation without fine-tuning: teach reusable skills from production traces instead of GPU-trained distilled models.
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AI agent memory that forgets on purpose — Pensieve manages four memory types with decay and self-pruning so RAG stops stuffing stale context.
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How to debug a multi-step AI agent after you implement ReAcTree: six production bugs the paper skipped, and what actually failed in the loop.
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Go platform architecture for a production AI agent codebase — patterns that keep rapid development sustainable without drowning in process.
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TOML vs YAML vs PKL for agent configuration: why we stopped fighting YAML, skipped PKL, and shipped on TOML.
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Go vs Python for AI agents: why we chose Go for a production agent runtime — concurrency, single-binary ops, deployment, and when Python still wins.
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LLM performance metrics for agents: TTFT, time per output token (TPOT), and the token-era equivalents of web vitals — what to measure when Lighthouse no longer applies.
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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.
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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.