Deploying Your Own AI Agent Marketplace for Codex, Claude, Copilot: What Actually Works in 2026

I’m Denis Shokhirev, Agentic AI Systems Architect based in Freiburg im Breisgau, running DennisCraft AI Studio. I build and operate B2B multi-agent systems on the Claude, Supabase, n8n, Doppler, and self-hosted Postgres stack. One morning in March, I had to hot-patch a live logistics agent that started pushing SQL code with CWE-89 injection patterns—this was production, not a demo (see live.gerdennisai.com). In 2026, building an AI agent marketplace for real clients means solving security, orchestration, and audit, not just connecting “cool” APIs.

Defining the AI Agent Marketplace: What Does “Marketplace” Really Mean?

For practitioners, an agent marketplace isn’t a slick UI with toggles for “Claude” or “Copilot”—it’s a platform where multiple LLM agents (e.g., Codex, Claude Code, Copilot) are available as switchable, auditable services for business tasks. This means dynamic agent routing, unified logging, and production-grade fallback. Anything less and your marketplace is a demo.

Production-Grade Architecture: Components That Ship

In production, you want event-driven agent invocations via orchestration (n8n). All calls and payloads are logged in Postgres (exposed via Supabase). Secrets are never shipped in env files; Doppler manages all sensitive material with strict access control.

Security Validation: What Actually Catches Bugs?

A 2024 Stanford CodeML paper found 38% of LLM-generated Python had CWE-89 (SQL injection) patterns (arxiv.org/abs/2401.09958). In my last three deployments, semgrep and bandit flagged SQL string concatenation and credential leaks within hours of go-live—this stuff escapes unit tests.

Real-World Static Analysis Pipeline

import subprocess

def run_semgrep(file_path):
    result = subprocess.run(
        ["semgrep", "--config", "p/ci", file_path],
        capture_output=True, text=True
    )
    return result.stdout

def run_bandit(file_path):
    result = subprocess.run(
        ["bandit", "-r", file_path],
        capture_output=True, text=True
    )
    return result.stdout

def main():
    code_file = "agent_job.py"
    print(run_semgrep(code_file))
    print(run_bandit(code_file))

Every code snippet generated by an agent is piped through this before execution. If bandit or semgrep flag an issue, the task is quarantined for human review. This is non-negotiable for any marketplace that claims production-readiness.

Orchestration and Fallback: Building Resilient Agent Chains

n8n as Task Bus

n8n sits at the core: every user job (e.g., “rewrite contract”, “generate SQL”) runs through a workflow, with nodes mapped to LLM agents. If an agent fails, n8n can auto-fallback to a secondary agent or trigger an incident alert (Slack, email), all logged for audit.

# n8n workflow fragment
- node: Claude_Code
  action: process_contract
  onError:
    - node: Copilot
      action: retry
    - node: Notify
      channel: slack
      message: "Claude_Code failed"

You must log all inputs/outputs and metadata for every agent call in Postgres (via Supabase)—without this, you have no traceability or compliance story when things go wrong.

Audit, Logging, and Compliance: The Real Bottleneck

Enterprise clients (DACH, E.U., and even CIS) demand: audit trails for 6–12 months, strict token separation, and zero plaintext secrets. Supabase with row-level security and connection auditing makes this possible out-of-the-box, but only if you use it right.

Row-Level Security Example

-- Restrict log access to current user
CREATE POLICY user_logs_policy
ON logs
FOR SELECT
USING (user_id = current_setting('app.current_user')::uuid);

On one client project, a single exposed Supabase endpoint would have dumped every agent log to the world. Row-level security is not optional if you want to sleep at night.

Agent Evaluation: Shipping Quality Across Multiple LLMs

Each agent (Claude, Copilot, Codex) has distinct strengths and quirks. You need a unified pipeline for static analysis but may require agent-specific configs. For example, Claude Code sometimes generates more verbose code, which can slip past default semgrep rules—tune your configs accordingly.

FAQ

Should I deploy agents as one API or orchestrate them?

Orchestration (n8n, Airflow) lets you swap, scale, and monitor agents independently. Bundling everything into one API increases coupling and risk.

How should I implement fallback?

Parallel calls to 2–3 agents and picking the first valid result minimizes latency but can hit API quotas. Sequential fallback is slower but cheaper.

How do I analyze agent-generated code quality?

Always run generated code through static analysis (semgrep, bandit). Use separate configs for each agent to handle their quirks.

What’s the main risk with token storage?

Key leakage through logs or misconfigured API endpoints. Doppler and Supabase row-level security reduce but don’t eliminate this risk.

How do I automate audit?

Stream logs to Supabase, export monthly audit tables, and trigger n8n alerts for suspicious patterns. Never rely on manual checks alone.

Which stage in your LLM pipeline catches the most issues in prod—static analysis, runtime sandbox, or human review? I’d genuinely like to know. I run a free 30-min stack audit for DACH founders building AI in regulated markets. DM me on LinkedIn or write to @ger_dennis_ai.