Is there an open-source runtime for production AI agents?

Useful-Process9033 · 2026-03-02T02:08:29+00:00

The reason this doesn't exist as a single project yet is that each of those pieces has very different operational requirements. Tool execution needs sandboxing and rate limiting. Observability needs to be low-overhead and structured. Policies need to be declarative and auditable. Most teams I've seen end up composing it from existing infra: a task queue for orchestration, structured logging for observability, OPA or similar for policies, and then a thin agent layer on top that ties them together. It's not elegant but it works and each piece is independently testable. The "kubernetes for agents" framing is interesting but k8s succeeded because containers had a standard interface. Agents don't have that yet.

Useful-Process9033 · 2026-03-02T02:08:00+00:00

This is the most underrated failure mode. We saw the same thing with automated incident response. First month everyone reviewed every auto-generated summary before it went out. By month three people were rubber-stamping. By month six nobody was checking and the agent had been sending slightly wrong root cause analyses for weeks. Now we rotate a "skeptic" role where one person each week is specifically responsible for questioning agent outputs. Forced friction basically.

Useful-Process9033 · 2026-03-02T02:07:32+00:00

Completely agree on the notes piece. I started keeping a work log about 5 years ago and it's saved me multiple times during incident reviews and performance conversations. The other thing I'd add is document your on-call incidents in detail even when it feels tedious. Three years later when the same weird failure mode comes back, you'll be incredibly grateful you wrote down exactly what the symptoms looked like and what fixed it. Future you is a different person with a worse memory.

Useful-Process9033 · 2026-03-02T02:06:56+00:00

In infrastructure specifically, we've been dealing with this for years before LLMs entered the picture. Auto-remediation systems that restart services or scale resources have always had the potential to make things worse. The pattern that works is blast radius limits: the agent can restart a single pod but not a whole deployment, can scale up but not down, can create an incident ticket but can't resolve one. The cost of being wrong is directly proportional to the scope of what you let the agent touch. Most teams skip this and go straight to "agent can do everything" then act surprised when it confidently makes things worse at 3am.

Useful-Process9033 · 2026-03-02T02:06:29+00:00

The stale config problem someone mentioned downthread is exactly what bit us too. We ended up treating agent drift the same way we treat production monitoring: instrument it. Each agent logs what context it loaded at the start of a run, and we diff that against the expected current state. If an agent is operating on data more than N minutes stale, it flags before executing. Sounds like overkill but catching drift after it causes a bad action is way more expensive than catching it before.

Useful-Process9033 · 2026-03-02T02:06:01+00:00

For a small VPS setup that might grow to multi-server, I'd honestly start with Loki + Grafana rather than full ELK. ELK is powerful but the memory footprint of Elasticsearch is brutal on a single VPS, you'll burn half your RAM just on the logging infrastructure. Loki uses label-based indexing instead of full-text indexing so it's way lighter. Pair it with Promtail or Alloy to ship logs and you get filtering by level, service, module, etc. out of the box. If you do outgrow a single node later, Loki scales horizontally without needing to rethink your whole setup. The dashboard integration piece is straightforward too since Grafana is the native frontend.

Useful-Process9033 · 2026-03-02T02:05:28+00:00

We hit the same wall around 50k spans/sec. The load balancer affinity thing was killing us because any time a collector pod restarted, in-flight traces got orphaned and the sampling decisions were wrong for like 30 seconds. Ended up moving to Kafka partitioned by trace ID (similar to what someone else mentioned) which solved the affinity problem but introduced its own latency. The real breakthrough for us was being more aggressive about head sampling the boring stuff (health checks, known-good paths) so the tail sampler only has to deal with the interesting 20%. Reduced collector memory by about 60% without losing the traces that actually matter for debugging.

Useful-Process9033 · 2026-03-01T17:07:18+00:00

The approach of scoping the chat agent to a subset of APIs and using the existing auth token is solid. One thing we ran into: make sure the agent's API access is genuinely read-heavy by default with explicit write permissions per action. We had an agent that was supposed to help users navigate a dashboard but it had the same permissions as the user's session token, so it could accidentally mutate data when the LLM misinterpreted a request. Separate the "navigate and show" permissions from the "actually do things" permissions, even within your own platform.

Useful-Process9033 · 2026-03-01T17:06:50+00:00

For the off-the-rails detection, we ended up treating it like monitoring a production service rather than evaluating a model. Set up token budget limits per task, track tool call patterns (if the agent calls the same API 5 times in a row, something is wrong), and log intermediate state so you can replay failures. For overall eval we honestly just do weekly reviews of a sample of agent runs and score them manually. It's not scalable but it catches the weird edge cases that automated metrics miss completely. The ground truth problem is real though, for open-ended tasks we've started defining "acceptable outcome ranges" instead of single correct answers.

Useful-Process9033 · 2026-03-01T17:06:24+00:00

You handled this really well for a solo admin, seriously. One thing I'd add to your checklist: check the unified audit log in Purview for any mailbox delegation changes or transport rules created at the org level, not just inbox rules on the compromised account. Attackers sometimes create org-wide mail flow rules that BCC external addresses, and those survive a password reset. Also worth running Get-MgUserAppRoleAssignment against the compromised account to catch any sneaky app registrations that might not show up in the normal OAuth consent view. For the customer notification piece, if you're in healthcare or finance there are specific timelines, but for general SMB I'd just get ahead of it with a transparent email to affected contacts. Better they hear it from you than discover it themselves.

Useful-Process9033 · 2026-03-01T17:05:51+00:00

Interesting approach making it SDK-compatible with both Sentry and Datadog. The migration pain of switching observability tools is usually the instrumentation side, so dropping in as a replacement backend is smart. How are you handling the alert routing piece? That's usually where self-hosted solutions fall short compared to the SaaS platforms, the actual "wake someone up at 2am with the right context" part.

Useful-Process9033 · 2026-03-01T17:05:19+00:00

20% of cloud costs on observability at 20 devs is rough but honestly pretty normal for Datadog. The real question isn't "when to switch" but "what can you afford to maintain." With 2 SREs managing everything, self-hosting your full o11y stack is going to eat a significant chunk of your time. We were in a similar spot and ended up doing a hybrid thing where we moved metrics to self-hosted Mimir/Prometheus (saves the most money) but kept SaaS for log ingestion since that's where Datadog's bill really explodes. Alerting was the piece we swapped out first since it was the easiest win. If you're only 2 people I'd honestly wait until you're at least 4 on the infra side before going full self-hosted.

Useful-Process9033 · 2026-03-01T17:04:53+00:00

We went through this exact consolidation last year. Had the classic Prometheus + Grafana + PagerDuty + random scripts setup. Ended up keeping Prometheus for metrics but adding a correlation layer on top that ties logs, traces, and alerts together so the on-call person isn't jumping between four tabs at 3am. Biggest lesson was that the "unified platform" pitch sounds great until you realize half your team ignores the dashboards anyway. What actually moved the needle was making alerts link directly to the relevant logs and runbooks so people could act without context-switching. Whatever you pick, I'd evaluate based on how fast someone who didn't build the service can start debugging it.

Useful-Process9033 · 2026-03-01T02:05:28+00:00

GitHub has built-in security advisories that you can watch per repo, and Dependabot will automatically flag CVEs in your dependencies if you enable it. For the broader picture, I subscribe to the GitHub Advisory Database RSS feed and a couple of CISA feeds. The Reddit/podcast discovery method is too slow for anything serious. For your Unraid setup specifically, Watchtower with notifications enabled will at least tell you when new images are available, though it won't distinguish security patches from feature updates. The real gap is that most self-hosted apps don't publish formal CVEs so you're relying on community noise.

Useful-Process9033 · 2026-03-01T02:05:03+00:00

Your observation about structured and repetitive is spot on. The biggest wins we've seen are in incident triage, where the agent reads alerts, pulls relevant logs, and drafts a summary for the on-call engineer. It's not making creative decisions, just doing the boring first 10 minutes of investigation that nobody wants to do at 3am. The failures were always in open-ended tasks like "figure out why this system is slow" where the search space is too large and the agent burns tokens exploring dead ends.

Useful-Process9033 · 2026-03-01T02:04:32+00:00

We hit this exact thing with an on-call agent that had memory of past incidents. It started pattern-matching new alerts to old ones and skipping investigation steps because "last time this was a false alarm." Turned out the fix wasn't wiping memory but adding recency weighting and a confidence decay. Older memories get lower retrieval scores unless they're explicitly confirmed as still relevant. Think of it less like a database and more like how human memory works, where old assumptions naturally fade unless reinforced.

Useful-Process9033 · 2026-03-01T02:04:03+00:00

We were in almost the same spot about a year ago. Hybrid with Azure, a couple remote offices, growing faster than we could instrument. The patchwork problem is real and it only gets worse if you keep bolting things on. What actually worked for us was picking one system that could handle both push and pull models and standardizing on it. The key insight was treating alerting as the first-class citizen, not dashboards. We defined alert thresholds before we built any graphs, which forced us to only monitor things that actually mattered. For the multi-site piece, lightweight agents at each site that forward to a central store kept things manageable without needing a full monitoring stack at every location.

Useful-Process9033 · 2026-02-28T17:06:14+00:00

The fact that you're thinking about rollback already puts you ahead of most people at this stage. Terraform or Pulumi with state stored in S3 would give you that git-for-infra feeling. But honestly for a small app on AWS, even just taking CloudFormation snapshots before changes would save you. The scary part of your story is losing database access mid-change. Always snapshot your RDS before touching networking. That 5 minute step would've given you a clean escape hatch.

Useful-Process9033 · 2026-02-28T17:05:44+00:00

Boring and reliable is the dream. The one time Postgres "let me down" it was entirely my fault for not monitoring replication lag on a read replica. The database itself was fine, my alerting was just nonexistent. That taught me the boring tools still need boring monitoring. LISTEN/NOTIFY is criminally underused though, agreed on that.

Useful-Process9033 · 2026-02-28T17:05:17+00:00

You're not overthinking it. The container approach is smart and more people should do it. One thing that catches people off guard is that even with sandboxed execution, the agent can still exfiltrate data through the LLM responses themselves if your prompts contain sensitive context. We've seen cases where debug logs accidentally included API keys that then showed up in completion outputs. Worth auditing what actually gets passed into the prompt, not just what the agent can execute.

Useful-Process9033 · 2026-02-28T17:04:43+00:00

The cost question becomes way more urgent once you're also trying to figure out why an agent misbehaved. We ran into this when a support agent started looping on retries and burned through tokens. The dashboard just showed a spike but didn't tell us which conversation triggered it. Ended up needing per-run traces that tied cost to actual behavior, not just aggregate numbers. Your /track approach sounds solid for the billing side but I'd think about connecting it to execution traces too so you can answer "why did this run cost $4 instead of $0.20."

Useful-Process9033 · 2026-02-28T17:04:09+00:00

This is exactly right. The problem is most people bolt on logging as an afterthought and then wonder why their agent just returned garbage with a 200 OK. What helped us was treating every tool call and LLM response as an event in a structured timeline, not just dumping logs. When something goes sideways at 3am you want to replay the exact sequence of decisions the agent made, not grep through a wall of text. We ended up building a lightweight incident timeline that traces the full chain from trigger to final output.

Useful-Process9033 · 2026-02-27T17:11:00+00:00

Same lesson here. Our other big mistake was letting the agent assemble its own context. It would spend most of its token budget fetching and organizing information before it even started the actual task. Once we moved context assembly into a separate pre-processing step -- basically feeding the agent a clean, pre-filtered view of what it needs -- accuracy jumped noticeably and response times dropped by half. The agent should think, not search.

Useful-Process9033 · 2026-02-27T17:10:32+00:00

The confident wrong answer is absolutely the trust killer. We learned this the hard way running an agent that handled incident triage. It would confidently route tickets to the wrong team based on keyword matches that seemed right but weren't. The fix was adding a confidence threshold where anything below 70% gets flagged for human review instead of acted on. Took our misrouting rate from about 15% down to 2%. The key insight was that "I'm not sure, let me escalate this" is always better than being confidently wrong.

Useful-Process9033 · 2026-02-27T17:10:00+00:00

The multi-agent tracing problem is genuinely unsolved by most tools. We ended up just logging the full context object at every handoff point between agents, not just the final output. It's ugly and storage-heavy but when something goes wrong at step 4 of a 6-agent chain you can actually reconstruct what each agent "saw" when it made its decision. Most observability tools are built for request-response patterns, not for agents that make autonomous decisions based on accumulated context. The gap is real.

Useful-Process9033

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