Your AI looked great in the demo. In production, it's giving wrong answers, burning tokens, and eroding trust. We find exactly why — and hand you the fix list.
of "AI agents" are deterministic workflows in disguise — burning tokens on reasoning that should be an if/else statement.
tokens is where context actually degrades — not the 200K on the box. Your AI is losing data before hitting the limit.
cost difference between running every subagent on Opus vs. right-sizing to Haiku. Most teams use the expensive model for everything.
errors logged when tool descriptions silently misroute calls. No alerts. No logs. Just wrong answers with total confidence.
Not hourly consulting. A defined engagement with a concrete deliverable and a personal guarantee.
4 engineers. 8 months of development. A multi-agent system that "worked great in staging." In production: dropping order numbers during context summarization, looping on conflicting instructions, approving its own broken output, burning $47K/month in tokens because every subagent ran on the most expensive model.
The fix wasn't a rewrite. It was structural: replaced 3 autonomous agents with deterministic workflows (they never needed reasoning), added a sprint system to prevent context rot, separated builder from evaluator, right-sized model selection per task.
Agents parsing natural language for completion instead of stop_reason. Subagents assuming shared memory. Self-evaluation bias — the builder grading its own homework.
Progressive summarization destroying dollar amounts and order numbers. Lost-in-the-middle effect burying critical instructions. Memory contradictions.
Tool descriptions causing silent misroutes. More than 4-5 tools per agent degrading selection. No distinction between "nothing found" and "the API failed."
"Revenue" means 3 different things across 3 teams. The AI picks a table and gives confident, plausible, wrong answers.
Full file reads at 3,000 tokens when grep costs 200. MCP servers consuming 2,000-8,000 tokens before any work starts. No batch API usage.
No structured logs. Same config for dev and prod. Silent catch blocks. Sentiment-based escalation. No session handoff artifacts.
Consultants bill hours. This is a fixed-scope engagement: defined deliverable, defined price, defined timeline. You get a written report with a prioritized fix list — not a slide deck, not an ongoing retainer you can't exit. If I don't find 3+ issues, you pay nothing.
Read access to your AI-related code repositories, architecture docs (if they exist), and logging/monitoring dashboards. I don't need production credentials or customer data. Most teams grant a short-lived GitHub collaborator invite and a Datadog/Grafana viewer role.
Then you don't pay. That's the guarantee. I've never had to honor it — every engagement so far has found more than 3 production-impacting issues. The structural failures are that common.
Your internal team built the system. That's exactly why they can't objectively diagnose it. The same session that wrote the code can't evaluate the code — that's one of the 6 failure patterns. An external diagnostic gives your team the prioritized fix list without the sunk-cost bias.
The average full diagnostic finds 60% token waste and 3+ silent failure modes. A $15K diagnostic on a $47K/month AI spend typically pays for itself in the first month. The Quick Scan at $2,500 is designed to be a no-brainer for any team spending $5K+/month on AI.
I do. Kevin Luddy. This isn't a firm that sells and delegates. One person does the diagnostic, writes the report, and walks you through the findings. That's why I limit slots.
"A smarter model doesn't fix agent failures. A smarter environment does."
Model upgrades are the most expensive way to avoid fixing your architecture.
"90% of automation needs are workflows, not agents."
If you can draw the decision tree, you don't need AI. You need an if/else statement that costs nothing.
"Agents cannot reliably judge their own output."
Confirmation bias with a GPU is not evaluation.
"More data degrades AI performance when context isn't managed."
Past a threshold, more information makes it worse, not smarter.
Tell me what you're running and what's not working. I'll respond within 24 hours.