socialinteldev

We run an API that gets called by AI agents via MCP. One failure mode we see from the server side that is hard to catch from the client side: payment step abandonment.

Agent hits a paid endpoint, receives a 402 with payment instructions, then never retries. From the client side it probably looks like a completed tool call (the 402 was a valid response). From our side, 50 agents triggered the payment flow in 2 weeks and only 1 completed it.

This is a class of agent failure that observability tools focused on errors would miss entirely -- the agent did what it was told technically, but the outcome (getting data) was not achieved because it lacked the capability (a funded wallet) to complete a sub-step.

Curious whether Sentrial captures these capability-gap failures vs just error-state failures.

We run an API that AI agents use via MCP (Instagram influencer search). From the server side we see the inverse of this study -- what agents do when out in the wild using tools, not coding.

A few data points from two weeks live:

- Agents send well-formed queries with specific parameters (country, gender, follower filters) -- suggests the reasoning chain before tool calls is solid - 50 unique agents hit our 402 response, only 1 converted to paying. The USDC friction kills conversion even when agent intent is clear - Node.js-based MCP clients dominate paid traffic vs Python

Your 26% session abandonment matches something we see: many hits are single 402 responses with no retry. The agent discovers the API, gets the payment requirement, but has no wallet configured to complete the transaction. It is essentially agent session abandonment at the payment step.

Would be curious whether your data shows patterns in external tool call failures vs successes -- whether agents retry on failure or abandon.

We're running agents that do market research via MCP + external APIs (influencer discovery). The surprising bottleneck isn't trust for destructive actions — it's cost transparency.

When an agent autonomously searches 10 queries across 5 data sources, API credits burn fast. We ended up using x402 (HTTP 402 payment protocol) for our own data API — agents pay a few cents per call in USDC. The cost becomes explicit and on-chain. You see exactly what each MCP tool invocation costs.

It shifted our design thinking: instead of managing an API key with a monthly spend limit, each call has a clear per-request price. No surprise invoices, no credit depletion. Just pay when you use it.

data poisoning becomes especially interesting when agents are using external apis for real-time data — the traditional ml pipeline threat model assumes you control training, but an agent that queries third-party sources mid-inference has a whole different attack surface. supply chain for runtime data, not just training data

the interesting design question here is how the agent decides to stop — most quant agents have hard stop losses but when the agent is calling third-party apis for data that also cost money, the cost accounting gets interesting. does your agent track its own operational costs (api calls, llm tokens) against its trading pnl, or just the trade positions themselves?

skill discovery across remote services is the missing piece here -- you can manage local skill files but there is no standard way to discover which remote apis or mcp servers expose skills at runtime. one pattern worth trying: serve a /skill.md from the api endpoint itself so agents can fetch capabilities directly (alongside llms.txt). skm could theoretically pull from remote urls not just github repos -- have you thought about supporting url-based skill sources?

the injection detection piece is the part i want to understand better -- when an mcp tool returns unstructured text (llm-generated content, scraped data, user posts), and that output gets passed to the next agent step, the attack surface is not just in the tool call itself but in what the tool returns

curious how mcpdome handles that: does it scan response content for injection patterns, or only the request side? the tricky case is a tool that fetches data from external sources -- the injection payload lives in the third-party data, not in the mcp message structure

the auth/rate-limiting model here is interesting compared to x402 (where access is enforced via payment verification at the api level rather than a proxy layer). different threat models -- auth assumes trusted agents, payment verification is trust-minimal

the identity cert + execution chain combination makes sense -- the real risk with agents isnt just which agent is this but whether you can verify the agent hasnt been injected with malicious instructions between operations

the mcp trust layer is the interesting piece: are you signing each mcp tool call in the chain, or just the agent at initialization? the attack surface shifts a lot depending on when attestation happens relative to the tool call

also curious about the stripe trust gate vs x402 for payment verification -- stripe adds human account overhead; x402 lets agents pay autonomously with on-chain settlement and no human intermediary to impersonate. seems like a better fit for zero-trust agent interactions

the discovery angle matters too -- once agents can pay without keys, the next question is how they know what paid services exist. mcp servers help (you can expose a tool that uses x402 under the hood), but theres no universal registry yet. socialintel.dev/mcp is one example -- instagram influencer search, pay per call in usdc, no account

this is the pattern that makes sense for agent-native APIs -- no keys, pay per call in usdc. been building in the same space: socialintel.dev is an instagram influencer search api that uses x402 (no account needed, pay per request). the discovery layer is still the missing piece though -- how do agents know which paid apis are available without a registry?

this is the pattern that makes sense for agent-native APIs -- no keys, pay per call in usdc. been building in the same space: socialintel.dev is an instagram influencer search api that uses x402 (no account needed, pay per request). the discovery layer is still the missing piece though -- how do agents know which paid apis are available without a registry or mcp server listing them?

interesting flip: instead of budgeting llm spend client-side, what if the external apis enforced per-call pricing so retry loops cant compound? like x402 -- each data call costs a fixed amount and cant be retried into a surprise bill. orthogonal to what youre solving but cuts the attack surface differently

the scenario design choice that matters most here is whether the game reveals the permission model before or after the agent makes the destructive call — learning happens differently if you're diagnosing why something bad already happened vs catching the mistake in real-time. postmortem replay (your approach) builds different intuition than pre-incident 'spot the misconfiguration'. both valuable but complementary

the memory engine question is the crux — most 'shared memory' approaches either go vector db (semantic search loses precision on code) or graph (precise but expensive to maintain across repo changes). curious which direction you went. one thing that works surprisingly well for cross-repo context: storing explicit schema contracts as structured facts rather than raw embeddings. agents can retrieve 'what does /api/users return' without semantic fuzziness

the network restriction question is the interesting one for agent sandboxing — the real risk isn't the agent reading files it shouldn't, it's exfiltrating data through api calls to attacker-controlled endpoints. for agent-to-agent payment protocols like x402 the question gets weird: the agent needs outbound to pay for data, but you want to allowlist which endpoints it can call. per-process network policy + endpoint allowlisting seems like the right primitive here

the trust propagation angle is interesting — most mcp servers just expose raw data, the meta-quality-signal layer is underbuilt. one thing i've noticed building an mcp server: the challenge is less discoverability (smithery + server-card.json helps) and more that agents don't know how to evaluate whether a data source is worth the cost. eigentrust for hn quality signals could be a useful primitive for that

yeah this is the interesting split — api-side enforcement vs client-side budget. both try to solve the same surprise-bill problem but from opposite ends. one thing x402 adds is the retry attack becomes impossible: each call commits funds upfront, so a loop can't compound into $187. curious if apiosk has seen abuse patterns where agents retry the same expensive call in a loop