And your observability stack is responsible for it. Foam captures and connects what your software does, then makes it queryable by agents.
We evaluated whether an agent identified the same precise root cause and would lead an engineer to the same fix.
Removing noisy Mattermost and Plane tools preserved task accuracy while sharply increasing the share of calls that helped solve the task.
It is fragmented across tools, represented differently, sampled, dropped, or never collected because you had to know in advance that it would matter.
Then we expect agents not to hallucinate and to piece everything together when the pieces are missing—or worse, misleading.That is not an agent problem. It is an evidence problem.
Ask Claude Code the same production question. Without Foam, it reconstructs a story from partial tools. With Foam, it reads the connected event.
Giving an agent access to five disconnected tools gives it five ways to reconstruct the same incomplete story.
Connect GitHub, GitLab, or Bitbucket. Foam discovers your services, writes the instrumentation, and opens tested PRs for your team to review and merge.
Connect GitHub, GitLab, or Bitbucket, then select the repositories Foam can scan and propose changes to.
It identifies frameworks, runtimes, queues, databases, browser apps, workers, and agent workloads before proposing any changes.
Foam chooses the right package and instrumentation for each service, tests it, then opens normal PRs. Your team only reviews and merges.
+ import { initFoam } from "@foam-ai/node";++ initFoam({+ serviceName: "inventory-api",+ capture: "advanced"+ }); import express from "express";3 PRs by foam[bot] ✓Installation PRs detect sensitive call sites and redact PII, PHI, credentials, and service-specific secrets at the source, so the raw value never leaves your application.
Collector-side NER can detect names, emails, phone numbers, cards, SSNs, IPs, and medical identifiers before storage.
If a value cannot be analyzed, Foam masks it as [REDACTED]. Redactions are marked for auditing and can be configured per service and environment.
HTTP requests and responses, database queries and results, queues, background workers, cron jobs, async workflows, user sessions, model calls, tool calls, logs, metrics, traces, and infrastructure activity.
Foam links the user, request, service, database, model, and downstream effects into one continuous trail.
Claude Code, Cursor, Codex, and other agents can query the complete record directly through one MCP.
OTel is the standard underneath. Foam is the complete system that makes it useful to your agents.
One init call wires the exporters, frameworks, databases, logs, browser, and model SDKs. Foam keeps checking as your stack changes.
Requests and responses, query results, sessions, model calls, and tool I/O—not only the spans, logs, and metrics most stacks retain.
Browser, services, database, agents, and infrastructure stay connected as one production record instead of five partial views.
Redaction, efficient storage, connection, and one SQL-query MCP are part of the system—not another platform project for your team.
Foam is not another agent reasoning over the data you already have. It creates the complete production record before an agent asks the question.
RAG retrieves information that already exists. Foam captures the requests, query results, sessions, and handoffs that normally never become available to retrieve.
Foam does not replace your engineering agents. It gives Claude Code, Cursor, Codex, and other agents the production evidence they are missing.
Foam does not sit above fragmented tools and ask an agent to reconcile them. It captures and connects the production sequence at the source.
Foam’s previous bug fixer was trusted by more than 50 startups. Now we are bringing the new Foam to market: complete production evidence built for coding agents.
Give them one coherent, queryable record of what your software actually did.
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