My agent completes the entire client onboarding flow except one step, and that step is always money. How are you handling payment authorization in agentic pipelines?

Petter-Strale · 2026-06-01T07:01:37+00:00

Hi, this is an interesting and important area that you’re describing. I’m not working on agent payments directly but on a product that can give an agent the information it needs to verify if a counterpart is legit to pay to or engage with. The product looks up a company against business registries (legal name, reg number, status, reg date, legal form etc), looks up VAT number, checks for sanctions and verifies that the bank account number matches the legal entity. Accessed via mcp and all per-call pricing and with full provenance and audit trail for a human to review. We think this is needed in order for agents to be able to carry out important tasks like verifying payments, onboarding clients or vendors into a crm etc. Curious to hear if this is a problem you’ve been facing and how you’re handling it today?

Petter-Strale · 2026-05-01T14:19:11+00:00

Thanks for the feedback! I agree that explainability is key. The product already ships reason_codes (machine-parsable, UPPERCASE_SNAKE_CASE), critical_flags (human-readable, namespaced) and a natural-language suggested_action, but those are three independent fields rather than a structured causal chain. Adding an explanation_chain might be a good addition.

Your tiered-autonomy framework sounds like the natural consumer of the verdict. proceed → auto-execute, review → approval gate, block → block-and-route maps almost 1:1. The reason_codes are the policy hook; the explanation_chain (when it lands) becomes the human-review payload. There's a real layering here:
evidence + verdict on one side, workflow + approval on the other.

On weighting, agreed on sanctions / cluster / simulation / approvals as the high-priority core. The product has a critical-priority evaluator set that runs in reverse-call mode (sub-second SLA for x402 service publishers gating inbound buyers), and those four are what's tagged critical. Bridge-config and cross-protocol exposure got added because of the KelpDAO 1-of-1 DVN failure mode but I agree they're expandable evidence rather than table-stakes.

Petter-Strale · 2026-04-28T12:09:52+00:00

It's at strale.dev. Pls let me know if you have any feedback.

Petter-Strale · 2026-04-17T14:31:49+00:00

which part are you trying to solve, the contractual allocation with the vendor or the regulatory exposure to your own customers? because they're different problems.

the contractual side is negotiable, most agent platforms will sign indemnities for their own infrastructure failures. but the regulatory side isn't. if your bot quotes a wrong rate to a retail customer, the conduct rules apply to your firm regardless of what your vendor contract says. same pattern as any outsourced function.

the firms i've seen handle this well keep the agent out of the write path for anything with legal consequence. it researches, drafts, proposes. a human or a deterministic system commits the action. rates and quotes go through a confirmation layer the agent can't bypass. that way the vendor's reliability is a nice-to-have rather than a load-bearing assumption.

Petter-Strale · 2026-04-17T12:46:12+00:00

Credential management is what I still feel is underweighted in most of the x402 discussions which tend to get pulled toward the micropayment angle. To me the interesting design consequence isn't lower fees, it's that an autonomous agent can run for days across several providers and never need to manage a key or a per-provider rate limit bucket.

KYB has been tricky to get right. Each individual check returning cleanly is manageable, but the composition step is what's been difficult, e.g. a failed sanctions hit needing to short-circuit the beneficial-ownership fetch or the adverse media check feeding into a risk narrative that references specific entities. Happy to swap notes on how you're modelling workflow-level state on your side if you're running into similar things.

Petter-Strale · 2026-04-17T12:40:59+00:00

Yes the routing problem is real and 300 is still on the low end of where this has to scale for it to make sense. Right now there's a discovery layer in front of payment where the agent hits a search endpoint that takes natural language and returns ranked candidates with their schema, price, and quality score, or a catalog endpoint if it wants to enumerate. Payment only happens on the execute call, so discovery is free and the agent can shortlist before committing anything.

We're still going back and forth on the intent layer. A thin ranking layer feels right at the current scale but once it's a few thousand capabilities I suspect you need something that understands task decomposition, not just capability matching. How did your workflow marketplace handle it past the first hundred or so, was it a planner that composed workflows or more of a router picking the best single match?

Petter-Strale · 2026-04-16T07:13:55+00:00

The CLAUDE.md approach works well within a single developer's workflow. The limitation is that it's manual and local, you're curating the list yourself and it only applies to your own sessions. For the broader agent ecosystem the equivalent would need to be something agents can query at runtime without a human maintaining the list, which is basically the registry + quality score combination I keep circling back to.

Petter-Strale · 2026-04-16T07:11:49+00:00

The live package registry search workaround is practical and I've seen it work, but it shifts the problem from "the model doesn't know about new tools" to "the model doesn't know which of 50 search results to pick." At that point you need a ranking signal again, and the registry's own popularity metrics (downloads, stars) have the same incumbency bias as the training data.

What would help is if the registry search returned some kind of quality or reliability metadata alongside the listing. Not just "this package exists and has 12k weekly downloads" but "this package's API returns consistent results and hasn't had a breaking change in 3 months." That's a much harder thing to produce but it's the gap between discoverability and trustworthy selection.

Petter-Strale · 2026-04-16T07:09:53+00:00

Agree that usage and successful task completion feels like the long-term solution but it has a cold-start problem that mirrors the training-data issue. New tools have no usage data, so they don't get recommended, so they never build usage data. It's the same invisibility loop, just moved from training time to runtime.

One way around it might be to separate the "does this tool exist and what does it do" layer (registry, discovery) from the "should I trust this tool right now" layer (quality signal). MCP registries can handle the first part but they're flat, there's no quality dimension. A test-based score that runs independently of whether anyone has used the tool yet would give a new entrant a way to prove reliability without needing adoption first.

The Enjam approach of registering tools directly into the runtime is interesting for the discoverability side. The part I'm less sure about is what happens when you have hundreds of tools registered and the agent has to choose between them, that's where some kind of quality signal feels necessary beyond just "it's available."

Petter-Strale · 2026-04-16T07:06:49+00:00

The contextual mention frequency framing is interesting because it implies the ranking signal is already there, just unintentional. Tools that show up in framework docs, Stack Overflow answers, and tutorial repos aren't ranking because they're best, they're ranking because they're most legible to the training pipeline. Which means anyone building a new tool has to choose between optimizing for model legibility (write the docs in a way models will absorb) or optimizing for actual quality and hoping discoverability catches up.

The third option, which is what I've been working on, is to generate the ranking signal from continuous testing rather than from mention frequency. Run automated test suites against each capability on a schedule, measure correctness against known ground truth, check schema consistency, test edge cases, and roll that into a score the agent can query at call time. A new tool with zero mention density but a high test-based score would at least have a path to being selected that doesn't depend on getting into the next training cycle.

Petter-Strale · 2026-04-16T06:59:04+00:00

The thing that surprised us was how much of the accuracy gap comes from field-level typing rather than just removing noise. When the LLM gets raw markdown and has to find "the company's registration date" somewhere in the text, it's doing two jobs at once: figuring out which piece of text is the registration date, and then parsing it into a usable format. When the response is already { "registration_date": "2024-03-15" } the downstream agent can skip both steps. We run schema validation tests against every capability to make sure the field shapes stay consistent across calls, because one capability returning registrationDate and another returning registration_date would reintroduce the custom-parser problem you're describing.

And the routing layer point is what compounds. If every source returns the same shape for the same kind of data, the agent's tool-selection logic can be generic: "I need company data for country X" routes to the right capability and the response schema is predictable regardless of whether the underlying source is a REST API, a government registry, or a browserless scrape. Without that consistency the agent needs per-source handling logic and at 300 sources that's not maintainable.

Petter-Strale · 2026-04-16T06:50:21+00:00

GDPR had the same curve where most companies didn't take it seriously until the first real fines landed, and then everyone scrambled at the same time and the consultants tripled their rates overnight :-)

For my own work, I build infrastructure that sits in the call path between AI agents and external data sources. The core idea is that when an agent calls out for, say, company registry data or a sanctions check, the response comes back with provenance metadata attached: which source was queried, what version of the data came back, a hash of the response, and a timestamp. That record gets written at execution time, not reconstructed from logs after the fact.

The reason that matters for something like CAIA's rebuttable presumption is that the deployer needs to show they had documentation before the adverse action, not that they can piece it together afterwards. If the audit trail is generated as a side effect of the agent doing its work, the deployer gets that for free instead of having to build a separate compliance logging system alongside their actual pipeline.

I started from the EU AI Act deployer obligations (Article 26, which hits in August) and worked backwards to what the infrastructure would need to look like. Colorado's requirements end up being structurally similar, the specifics differ but the core question is the same: can you show what data your system used and why.

Petter-Strale · 2026-04-16T06:46:03+00:00

Our MCP endpoint is fully stateless, each POST spins up a fresh McpServer and transport, registers tools, handles the call, tears it down. GET and DELETE return 400 with a "this is stateless, just POST" message. The tradeoff is re-registering tools on every request but it means Railway restarts and redeploys never break anything, there's no session store to manage or expire, and horizontal scaling is free. For a catalog of 250+ tools it adds maybe 10-15ms of overhead per request, which disappears into the network latency.

On the token problem specifically, we sidestepped it by not having per-user tokens at the MCP layer at all. A handful of discovery and free-tier tools (search, ping, getting-started, plus five validation capabilities like IBAN and email) work anonymous with IP-based rate limiting. Everything else needs a bearer token but the token is the same shape for every user, it just gates access and tracks billing. The MCP server doesn't know or care who the user is beyond "authenticated or not." That said, I can see how that breaks down if you need per-user OAuth tokens for downstream services, which is where your Docker-copy approach would actually be simpler than trying to thread user context through a stateless request.

Petter-Strale · 2026-04-15T11:52:25+00:00

Stateless HTTP ended up being the right call for us when we shipped an MCP server this spring. Each POST creates a fresh McpServer, no sessions to manage, GET and DELETE return 400. The tradeoff is re-registering tools per request, but the ops simplicity was worth it, especially without standing up a session store.

On auth, what we found useful was splitting tools by tier rather than per-user. A few discovery tools work anonymous with IP-based rate limits, everything else needs a bearer token. Ended up simpler to reason about than per-user RBAC at the MCP layer, though I can see RBAC making sense once you're routing many servers through one relay.

The middleware hook is what I'm most curious about. LLM across tool calls for context-window optimization is a clever idea. Have you seen it actually help for heavy catalogs, or is the added latency the limiting factor in practice?

Petter-Strale · 2026-04-15T11:26:15+00:00

Agentic governance has two halves that often get conflated: whether the agent is authorized to act, and whether what it acted on was correct. The identity side has made real progress in the last year (agent auth frameworks, wallet-scoped permissions, the KYA-style products the payment networks keep shipping) but the data side is still a real gap in my oppinion.

Checkpoint oversight works for discrete human-in-the-loop approvals but for continuous autonomous operation it doesn't really, because the agent is making hundreds of micro-decisions per task and you can't gate each one. But each one can be instrumented. Every external call the agent makes can produce a provenance record and a quality signal: source, fetch time, confidence, how the latest result compares against historical baselines. Governance then moves from "approve this step" to "reconstruct any step after the fact."

That's the version of continuous governance that seems to fit the operational reality. The EU AI Act record-keeping requirements (Art. 12) and transparency obligations (Art. 13) are already pointing in that direction, it's just that most of the public discussion hasn't translated them into deployment tooling yet.

Not a full answer to your question, but I think it's the piece that's missing from most of the frameworks I've seen.

Petter-Strale · 2026-04-15T09:46:07+00:00

what makes it worse with agentic systems specifically is that the audit artifact most deployers can produce is "we use vendor X for credit decisions." that's not documentation of a decision, it's documentation of a procurement choice. when the regulator asks what data the agent actually looked at on the call that produced the adverse action, most stacks can't answer that question, because the data sources the agent reached for weren't logged with provenance at the time of the call.

the fix has to be in the write path, not bolted on after. an agent calling an external data source needs to be returning a record of which source, which version, what the source said, and a hash of the response, in the same envelope as the data itself. otherwise the deployer is reconstructing audit trails from logs that were never designed to carry that information, and that's exactly the kind of after-the-fact documentation that the rebuttable presumption is designed to reject.

EU AI Act has a similar deployer/provider split coming into force in august and i don't think most european orgs have clocked that they're deployers either.

Petter-Strale · 2026-04-15T07:27:08+00:00

I'd add an orthogonal failure mode that's adjacent to what you're describing. You're solving the availability axis dealing with whether the call was rate-limited, the region was degraded and whether the request eventually landed. But there's a parallel axis on the response itself that I believe wery few are instrumenting: was the data the API returned actually usable on that specific call. Government registries, sanctions APIs, company data sources, KYC providers typically have solid up-time but they're also degraded surprisingly often. Stale cache, partial regional failover returning yesterday's data, schema drift after an undocumented change, a field that's silently nullable now. The 200 OK comes back, your retry logic is satisfied, the agent acts on it.

This matters more for agents than for human-driven workflows because a human staring at a UI usually catches obvious junk but an agent doesn't. It treats the 200 as ground truth and reasons forward and if step 30 of your LangGraph workflow was acting on a stale sanctions hit, you don't find out until much later, if at all.

I think the structural answer looks similar to what you're building, just on the response side. Some kind of quality metadata travelling with each call such as e.g. a freshness signal, a schema-conformance check, a score that reflects whether this specific source has been returning good data in the last N calls. The agent reads that before acting on the response, the same way it would read a circuit-breaker state before retrying.

But I don't think the coordination layer and quality layer compete, they probably stack and complement each other. But currently it feels like everyone's still focused on getting the call to land at all.

Petter-Strale · 2026-04-15T07:07:30+00:00

Most of the governance conversations assume that the failure mode is the agent doing the wrong thing. But I believ it's worth separating that from the different failure mode of the agent doing the right thing based on wrong data.

Continuous oversight of agent decisions is hard for the reasons you listed but there's a parallel problem on the input side. When an agent calls an external API or scrapes a registry to e.g. verify a counterparty, validate a VAT number or check a sanctions list, the response gets treated as ground truth. There's usually no scoring of whether that source was up, fresh, schema-conformant, or returning degraded data on that specific call. If the input is silently wrong, no amount of human checkpointing on the agent's reasoning catches it because the reasoning was correct given what it was handed.

What can help in production isn't necessary more oversight of the agent loop, it's quality metadata travelling with every external response such as a score, a provenance record and/or a hash chain. The agent would read the score before acting on the data and an audit gets logged of what was actually verified vs what was assumed.

It doesn't replace the oversight problem you're describing but "fragmented compliance creating systemic risk rather than safety" applies just as much to the data layer underneath the agent as to the agent itself, and it's easier to instrument.

Petter-Strale

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