Framework

When an AI coding agent is allowed to scaffold, write, or configure parts of your product, the agent is making consequential engineering and vendor choices. This checklist turns those choices into evidence you can audit, quantify, and govern—so cost, lock‑in, compliance, and maintainability stop being surprises.

Below are 12 categories to include in any AI coding‑agent tool‑pick audit, the practical checks to run, the kinds of artifacts to collect, and short notes about what matters specifically for Paraguay teams.

1) Tool‑pick habits and platform signals - What to test: sample the agent across representative engineering prompts and extract the primary service or provider it picks for each category (hosting, CI/CD, object storage, edge runtime, analytics, auth). Run the same prompts multiple times and with minor prompt changes to measure consistency. - Evidence to collect: prompt logs, agent responses, picked provider names, timestamps, and confidence/metadata if available. - Why it matters: vendor defaults imply procurement paths, latency, and potential contractual lock‑in. - Paraguay note: measure whether recommended providers have nearby regions or local partners. If the agent repeatedly picks a provider without local presence, plan for higher latency and potential support friction. - Related research note: Amplifying’s Codex vs Claude analysis reports directional platform preference signals (e.g., Cloudflare vs Vercel tendencies) and provides counts of tool picks across sampled runs; use those results as a benchmark rather than a final verdict.

2) Build vs buy preference - What to test: does the agent default to a custom implementation (DIY) or recommend a managed service for a given capability (search, auth, analytics)? - Evidence: code snippets showing custom logic, package.json or requirements, or provider API calls. - Impact: custom builds raise maintenance costs; managed services raise recurring costs and lock‑in. - Paraguay note: small teams in Paraguay may prefer managed services for speed, but budget and recurring USD payments can be a constraint—capture TCO scenarios in local currency terms. - Research note: Amplifying found that many category top‑pick agreements favored custom solutions in the sampled responses; treat that as a pattern to test in your stack.

3) Cloud and hosting defaults - What to test: recommended runtimes (edge vs serverless vs instances), CDN choices, regional availability, and configuration defaults (e.g., automatic deployments to a specific provider). - Evidence: deployment manifests, Dockerfiles, serverless function signatures, and provider CLI commands. - Paraguay note: evaluate whether the recommended cloud has presence in South America or good peering to Asunción. If not, quantify expected latency and egress costs.

4) Dependency, package, and license risk - What to test: list all suggested libraries, their versions, and licenses. Check for known risky or unmaintained packages. - Evidence: generated package manifests, import statements, or requirements files. - Impact: insecure or unlicensed dependencies increase legal and operational risk. - Paraguay note: legal teams should review copyleft or restrictive licenses against local contracting practices and potential reselling or customization scenarios.

5) Secrets, credentials, and configuration handling - What to test: does the agent bake secrets into code examples or recommend insecure secret management practices? Does it suggest environment variable usage and provider‑specific secret stores? - Evidence: sample code, CI config, and the agent’s suggested secrets flow. - Impact: accidental leakage in generated code is common; catch it in test runs and prompt logs. - Paraguay note: when teams use shared development workstations or WhatsApp for coordination, reinforce secret hygiene and short‑term token rotation procedures.

6) Data exposure and privacy - What to test: whether the agent suggests third‑party analytics, logging, or debugging tools that forward PII or product data offsite. - Evidence: calls to external APIs, telemetry SDK recommendations, or sample debug outputs. - Impact: regulatory and customer trust risk; requires mapping data flows. - Paraguay note: translate privacy impacts into procurement checkpoints—who signs the data processing agreement, and is data stored in the region or cross‑border?

7) Cost model and scaling assumptions - What to test: which cost model the agent assumes (per‑call serverless, instance hours, monthly managed service), and whether it produces any cost estimates. - Evidence: explicit cost models in responses, sizing assumptions, or suggested quotas. - Impact: hidden recurring costs are among the most common surprises. - Paraguay note: convert cost estimates to PYG or local procurement categories for budget review. Flag solutions whose recurring costs are USD‑priced without local billing options.

8) Operational maturity and required skill sets - What to test: does the agent recommend tools that match the team’s skill level? For example, low‑ops platforms vs systems requiring SRE expertise. - Evidence: the agent’s commentary on maintenance, update cadence, and staffing. - Impact: misaligned tooling increases time‑to‑market or forces unplanned hires. - Paraguay note: document whether local contractors or partners can support the recommended stack; if not, include training or managed options in the proposal.

9) Integration and vendor lock‑in surface - What to test: how tightly the suggested solution couples code to a vendor SDK or proprietary format (e.g., vendor‑specific data stores, CLI commands embedded in deployment). - Evidence: code that uses proprietary SDKs without abstraction layers; migration steps suggested (or lack thereof). - Impact: future migration cost, bargaining power, and exit options. - Paraguay note: prefer modular architectures and explicit migration paths for public procurement or long‑term contracts.

10) Observability, monitoring, and error handling - What to test: whether the agent includes logging, error reporting, health checks, and observability tooling in its recommended stack. - Evidence: sample log formats, suggested alert rules, APM choices. - Impact: lack of observability increases mean time to repair and customer impact. - Paraguay note: ensure logs and metrics aggregation meet any sector rules (finance, health) and that local networks can transmit telemetry reliably.

11) Compliance, contractual, and regional constraints - What to test: whether the agent’s recommendations consider data residency, contractual obligations, or sector regulations. - Evidence: explicit statements about compliance, DPA suggestions, or region‑specific advisories in responses. - Impact: noncompliance can block deployments or trigger legal exposure. - Paraguay note: include checks for payment processing rules, tax treatment of foreign SaaS, and sector‑specific obligations (e.g., public sector procurement norms).

12) Human review, governance, and repeatability - What to test: the auditability of the agent’s decision (prompt history, versioned outputs), how to incorporate human review gates, and whether the agent produces reproducible artifacts. - Evidence: stored prompt/response logs, CI gates, code review policies, and a repeatable test harness. - Impact: reproducibility and governance reduce operational surprises and protect product quality. - Paraguay note: embed decision records into procurement documentation and handover artefacts for local auditors or partners.

Practical audit workflow (short) - Scope: pick 6 representative prompts that reflect your most common engineering tasks (deploy, set up auth, implement search, add analytics, create an API endpoint, write tests). - Run: execute each prompt against the agent several times and with minor variations. - Capture: store prompt text, agent output, extracted vendor/service names, any generated manifests, and generated code files. - Score: assign three impact dimensions to each pick — Cost (low/medium/high), Risk (low/medium/high), and Time‑to‑Ship (fast/medium/slow). - Report: produce a one‑page risk matrix that maps categories to recommended mitigations (policy, alternative picks, manual review, procurement checks).

What to expect from the evidence - Use the audit to produce a short list of blocking issues (secrets, data egress, license problems), operational shifts (new managed services or SRE needs), and quick wins (replace a risky dependency, require secret scanning in CI). - Treat Amplifying’s benchmark data as a directional reference. Their Codex vs Claude study provides measurable samples of agent choices and can help flag where a consistent bias toward a vendor in the wild may indicate broader industry defaults.

How LeadWise frames the business decision (for Paraguayan buyers) - Translate audit findings into three clear buy‑options for decision‑makers: quick‑launch (managed services + guardrails), balanced (abstraction + mixed managed/custom), and durable (low lock‑in, higher initial engineering). - For each option, model first‑year cost in local procurement terms, list required roles (in‑house, contractor, partner), and state the principal operational risk.

Next step If you need a repeatable, procurement‑ready audit that produces prompt logs, a risk matrix, and an actionable roadmap, plan an AI tool‑pick audit. A compact engagement produces a 2–4 page executive brief you can use in procurement and a technical appendix your engineers can act on immediately.

Related reading - What AI Coding Agents Actually Choose, Explained For CEOs (/en/blog/what-ai-coding-agents-actually-choose-explained-for-ceos) - Codex vs Claude Code: The cloud preference signal managers should notice (/en/blog/codex-vs-claude-code-the-cloud-preference-signal-managers-should-notice)

Sources - https://amplifying.ai/research/codex-vs-claude-code-picks