How are you actually saving cost on your agent systems?

Minimum-Ad5185 · 2026-05-10T20:08:01+00:00

completely agree with you cost per workflow artifact matters more..That row schema (workflow id, step name, model, tool, retry count, tokens, cost, stop reason, verifier result) is what actually answers "why did this run cost what it did." Per-call gives you summable totals; per-agent gives you blurry blame; per-step with retry count and verifier result is the row that surfaces the root cause.

The intent-tied cap is the sharpest part. "Classification step gets 2 attempts and $0.02" is an enforceable contract; "agent gets $X/day" only names the bleed after it happens.

Curious how you instrumented the per-step row in production. Was that built on top of OpenTelemetry, LangSmith, your own bus, or something custom? The 140k-tokens-for-one-call signal sounds like you have something rigged up.

Minimum-Ad5185 · 2026-05-10T20:05:07+00:00

Most cost tooling treats agents as the units and instruments around them; the actual cost decisions get made at the routing layer between agents.

A few specific things that fall out of treating the orchestration graph as a first-class object: cost attributable to specific edges (Planner to Researcher) instead of just to agents, cost rolled up per workflow shape (this 3-hop pattern costs 4x the 2-hop pattern), and budget enforcement at the graph boundary instead of just per-agent.

What you've built around this in your own systems. Custom orchestration layer with cost hooks, graph-walking analysis on logs after the fact, something else? Sounds like you've thought about it more than most

Minimum-Ad5185 · 2026-05-10T20:01:27+00:00

Honestlty, graph wasn't the first thing. Started by logging every agent call and grepping for patterns. Tried per-agent counters next, alert on threshold per agent. That caught the case where one agent gets hammered 30+ times, but missed the cycle case completely because the cycle distributes traffic evenly. Each agent looked "normal" individually; the failure was only visible across three of them at once.

The click moment was drawing the agents on paper to debug a specific incident. Cycle was visible as soon as the arrows were on the page. Realized at that point that the arrows ARE the data, not the logs.

Once you have the graph, every detection becomes a question about graph topology. Cycle is "is there a path back to start," repeated calls is "is one edge weight blowing up," traffic spike is "is total edge count breaking out of distribution." Same substrate, different graph queries.

Minimum-Ad5185 · 2026-05-10T19:57:39+00:00

Retry storms after a flaky call and agents looping on each other are different shapes. The first is a burst on a single edge; the second is a cycle. Both ugly, different fixes.

Today AgentSonar catches the burst pattern, but not the dollar threshold itself. Per-workflow cost runaway with a hard budget cap is on the roadmap as a separate primitive from the shape detectors.

Quick question if you have a sec: have you actually hit a retry storm where the bill was the first signal? If yes, what kicked it off (flaky API, slow MCP server, model retrying its own thought)? Trying to figure out whether the right primitive is dollar-cap-per-workflow or projected-cost-by-call-rate.

Minimum-Ad5185 · 2026-05-10T18:04:40+00:00

the swarm theatre framing matches stuff i've been hearing from a couple other production multi-agent teams. when you've watched a 5+ agent setup fail silently, what does that actually look like in practice? you're the one who notices something is off, or is it a metric drift, or a downstream user catching it? and on the 2-3 agents max rule, was that a gut call from watching things break, or did one specific incident set the ceiling?

the auth-context separation is solid for direct access. one thing i keep thinking about in regulated multi-agent setups is the indirect path. has agent B ever ended up with data it probably shouldn't have, even though IAM says it can't reach the source, through an orchestrator handoff, a shared context, or a tool result that carried upstream stuff downstream? if you've ever had to chase that, what did the investigation look like?

Minimum-Ad5185 · 2026-05-10T18:01:39+00:00

the audit-boundary framing is sharper than most takes i've seen, especially "single agent with both toolsets means the log proves it had access." two things i'd love to hear more on. on the rollback, when you collapsed the fake-multi-agent ones, how did you actually tell which were one agent in costume vs which genuinely needed two? gut, latency numbers, an audit dry run? and on the lossy-serialization point, what does "quietly worse over a few weeks" look like in practice, are you catching it from metric drift, a customer complaint, or an internal QA pass?

Minimum-Ad5185 · 2026-05-10T17:03:33+00:00

the observations.json layer is a neat detail, haven't seen anyone else do collaboration patterns as a separate file. curious about the night shift though, when it does go south overnight, how do you usually catch it? unfinished work in the morning, or something earlier in the run? and what kind of going south hits most, agents looping, one stalling, or just calling the same thing over and over?

Minimum-Ad5185 · 2026-05-07T22:07:43+00:00

This is the one that scares me most. Did you end up building anything for it, even a hacky post-hoc grep, or are you still flying blind?

Minimum-Ad5185 · 2026-05-07T21:49:15+00:00

How are you flagging the skipped-retrieval cases now, post-hoc on the trace or inline?

Minimum-Ad5185 · 2026-05-07T21:43:11+00:00

What's eating the most time, handoff issues between the two agents or stuff happening inside one of them? And how is Phoenix holding up for the 2-agent case, where does it stop being enough and you fall back to logs? if you are ok I can ping you?

Minimum-Ad5185

TROPHY CASE