AI Agents, MCP & RAG Architecture Review

AI agents, MCP and RAG review

Agents fail in ways normal software doesn't: a tool call that returns something the model misreads, a retrieval step that silently drifts, a pipeline that loses data while every test passes. Reviewers here build agents, MCP servers and RAG systems in production, and review yours for reliability, evaluation and safe data handling.

04 · experts matched 50 cases across their profiles Contracted through Invental Request a review ↗

Lead match

Code review · Arch. review · Arch. design · Due diligence · Vibe-code rescue · Mentoring

0→1 founder-CTO for data-heavy SaaS and agent-ready tooling

Best for: solo founders and seed startups building a data or AI product · SaaS teams adding an API or MCP server · developer-tool companies whose CLI or API will be called by AI agents · founders who want product and engineering advice in one person.

founder-CTO / founding product engineer · Europe View profile →

Lead match · 01 founder-CTO / founding product engineer

From the network

Selected cases from these experts’ profiles.

Agent-readiness review of CLIs, APIs and MCP servers

AI agents call CLIs and APIs constantly, and many tools hang on prompts, return ambiguous exit codes or emit unparseable text. Our expert tests tools the way an agent uses them and reviews them against a practical checklist: non-interactive mode, retry-safe and side-effect-aware error semantics, one machine-readable response shape, input validation and least-privilege credentials.

Track record · developer tools / AI agents · any tool vendorPython, JSON Schema, conformance tests

0→1 founder-CTO for data-heavy SaaS and agent-ready tooling →

Multi-agent reliability review

An agent system works in demos but fails unpredictably in production.

Map every model and tool call, check output validation, retry limits, timeouts, fallbacks and guardrails, and look for loops and silent failures.

You get

A failure-path map, a ranked fix list, and suggested evaluation checks to keep it honest.

Bookable · Architecture review · PR/MR code reviewTypeScript / Python agent frameworks, message queues, LLM APIs

Agent-reliability engineer →

Auditing a live lead pipeline that lost data while every test passed

A pipeline scraping and enriching thousands of businesses twice a day looked healthy: builds green. One of our experts audited it and found a fallback dedup key wrongly merging several hundred distinct businesses, an LLM output limit truncating more than half of enrichment results, a join multiplying a fact table many times over, and a data contract badly undercounting the most valuable leads. They rebuilt it as a medallion lakehouse with grain tests.

Track record · B2B lead generation · small businessDelta Lake, DuckDB, dbt, Dagster, LLM enrichment

Agent-reliability engineer →

Multi-agent coding pipeline with real tests

Two early-stage products had to ship fast with a very small team. Our expert ran several coding agents that write, review each other, ship and deploy, while the expert owned decomposition and tests. They also evaluated a commercial agentic IDE for about a week against senior-engineer practice and found that it skipped tests, padded scope and marked crashed tasks as done. Their fixes: smaller tasks, separate planning sessions, and raising "not implemented" instead of fake success.

Track record · AI-native startups · seedClaude Code, Codex, Python, CI/CD

0→1 founder-CTO for data-heavy SaaS and agent-ready tooling →

Single-purpose AI agents running a solo product's operations

A job-search product the expert runs alone needs constant ingestion, enrichment, publishing and monitoring. One of our experts split operations across a handful of single-purpose AI agents, each with one narrow job. Outcome: by their own estimate, most routine operations run without them.

Track record · recruiting / job marketplace · solo founderTypeScript, Python, LLM APIs, scheduled workers, edge compute

AI-native staff engineer →

Questions buyers ask

What makes an AI agent unreliable in production?+

Unclear tool interfaces, no evaluation set, missing retries and timeouts around model and tool calls, and no logging of what the agent actually did. Most failures come from the interfaces around the model, not the model itself.

What is an agent-readiness review?+

A review of your APIs, CLIs or MCP server from the point of view of an AI agent that has to use them: predictable inputs and outputs, machine-readable errors, safe defaults and documentation an agent can follow.

Why do AI agents fail when they call command-line tools?+

Most CLIs were designed for humans. They page output and wait for a keypress, return the same exit code for "nothing happened" and "half-done", print free text instead of structured data, or crash silently on odd input. An agent then stalls, wastes tokens, or retries a non-idempotent action. A review checks exit codes, output format, timeouts and retry safety against a list of known failure modes.

Tell us what needs a look.

Describe the system and the question. We match a lead expert from this page, or a better fit from the network, and confirm scope before anything starts.

— Get in touch

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— What to include

A link or short description of the code or system, what you want checked, your stack, and when you need the answer. No repository access is needed until scope is agreed.