Last month, a VP of Engineering at a 100-person company told our team at Lyra something brutal:
"I just calculated we're wasting over $200K a month on AI-gen code rework due to incomplete and out-of-date specs…"
If you're a CTO or VP of Engineering frustrated with AI agent adoption, you've probably seen this pattern:
Agents work great for boilerplate and junior-level tasks
But anything complex? The code looks good, but doesn't work
Your engineers are stuck in clarification loops and rework cycles
The promised 40% productivity boost feels like a fantasy
Here's what's actually happening: Your AI agents aren't the problem. Your specs are.
The Hidden Bottleneck: Garbage In, Garbage Out at Scale
AI coding agents will build exactly what you tell them to build—nothing more, nothing less. However, if your initial requirements are vague, agents will make decisions on your behalf, potentially leading to the assumptions and output.
When a human engineer reads a vague PRD, they fill in the gaps. They make assumptions based on domain knowledge, past projects, and tribal knowledge. Sometimes they guess right. Often they don't, but at least they know they're guessing and can ask questions.
AI agents don't do this. They just... build. And when your spec says "users should be able to reset their password" without mentioning:
Rate limiting after failed attempts
What happens if the email doesn't exist
Mobile vs. web flow differences
Token expiration timing
...your agent builds something that technically works but creates a production incident two weeks later.
The math is brutal. For just one product team:
5 engineers at $150/hour
5 hours per week per engineer wasted on clarifications and rework
That's $3,750/week or $15,000/month evaporating because your specs had holes in them
And it gets worse as you scale.
Why This Problem Multiplies in Larger Organizations
At a 50-person company, your senior engineers know the product intimately. Vague specs get caught in code review. Tribal knowledge fills the gaps.
At 500+ engineers? That breaks down completely.
Take Capital One: 15,000 engineers. You can't assume everyone has the same context. Your velocity is defined by your weakest teams, not your strongest. And those weaker teams are:
Junior engineers who don't know what questions to ask
Engineers two teams removed from the original product decision
Offshore teams working in different timezones from your PMs
Every handoff layer—PM → Tech Lead → Staff Engineer → IC → AI Agent—is another place where context gets lost and assumptions creep in.
The result? Your AI agents inherit all these ambiguities and amplify them into production bugs.
What Actually Happens When Specs Are Bad
Let me show you a real example from a customer session:
A fintech company brought us their PRD for a new KYC verification flow. Looked reasonable at first glance—standard identity verification requirements, integration points, acceptance criteria.
We ran it through Lyra's analysis. In 3 minutes, it flagged:
7 missing edge cases (What happens when a user submits documents in the middle of verification? What if they switch browsers? What about users in states with different ID requirements?)
3 conflicts with existing authentication logic buried in their codebase (The new flow assumed single-session verification, but their existing auth allowed cross-device sessions)
12 ambiguous requirements that would force engineers to make product decisions (Does "government ID" include passport cards? Military IDs?)
Their PM's response? "Oh shit. We would have spent at least a week going back and forth on this."
That's the hidden cost. Not just the rework—the time spent clarifying what should have been clear from the start.
Their engineers would have:
Built something based on their interpretation
Submitted PR for review
Senior engineer spots the gaps
Back to PM for clarification
Refactor and retest
Repeat 2-3 times
With AI agents, this cycle happens faster but more frequently because the agent can't spot the gaps ahead of time.
What Happens If You Don't Fix This
Here's the timeline you're on if you don't address the spec problem:
Your AI agent adoption stalls at 15-20%. Your engineers lose trust in the output. You quietly stop renewing Cursor or Copilot seats because "they're not delivering ROI."
Meanwhile, your competitors figure this out. They clean up their spec pipeline. Their AI agents start shipping features at 2x your velocity.
In 18 months, you're explaining to your board why your $500K AI investment went nowhere while competitors are eating your lunch.
The gap between companies that figure out the spec layer and those that don't? That's going to define who wins in the next 3 years.
The Missing Layer: Spec Intelligence Before Code Generation
Here's what needs to happen before any AI agent touches your codebase:
1. Catch ambiguities before engineering sees them
When a PM writes a spec, it should be analyzed for:
Missing edge cases (rate limits, error states, cross-platform differences)
Conflicts with existing system behavior
Vague acceptance criteria that leave decisions to engineers
2. Ask clarifying questions while context is fresh
The PM should answer "What happens when X?" BEFORE engineers start guessing. Not after the PR is submitted.
Lyra does this by analyzing the PRD and asking pointed questions:
"You mentioned password reset emails—what's the token expiration?"
"This conflicts with your existing 2FA flow. Which takes precedence?"
"Mobile behavior isn't specified for this feature. Is it the same as web?"
3. Generate tech specs that include codebase context
Here's where it gets interesting. Lyra does codebase discovery by prompting agents like Cursor or Claude Code to analyze your existing implementation, then uses that output to generate tech specs that account for:
How your auth system actually works today
Where the new code needs to integrate
What existing patterns to follow
4. Break it down into AI-ready implementation tasks
Once the PRD and tech spec are solid, Lyra creates one-shot prompts tailored for AI coding agents—synced to Jira or Linear so your agents have complete, unambiguous instructions.
5. Propagate changes when decisions evolve
Product decisions don't stop after the spec is written. They happen in Slack threads, Zoom calls, and design reviews.
Lyra captures those decisions and propagates updates across all related docs—PRD, tech spec, implementation tasks. Your AI agents always work from the latest, most complete context.
The Secret Weapon: Institutional Knowledge That Compounds
Here's what separates Lyra from traditional spec tools: it gets smarter with every project.
When your team collaborates on a PRD—commenting in Google Docs, suggesting edits, debating tradeoffs—Lyra captures all of it:
The previous version (what was initially proposed)
The comments and discussion (why alternatives were considered)
The final decision (what shipped and why)
This becomes your institutional knowledge base. On your next project, Lyra references this context:
"Last time you built a verification flow, you decided against allowing cross-device sessions due to fraud risk. Is that still the constraint here?"
"Your team historically requires rate limiting on any user-facing endpoint. I don't see that specified—should I add it?"
"Three projects ago, you hit issues with timezone handling in notifications. Have you addressed that in this spec?"
This is how you turn junior engineers into mid-level performers—by giving them access to decisions that would normally live in senior engineers' heads or buried in Slack history.
By project 5, Lyra is catching 3x more issues because it knows your system, your patterns, and your team's decision-making principles.
Without this layer, every new engineer has to relearn these lessons. With Lyra, institutional knowledge becomes infrastructure.
Why This Is Actually About Trust
The real reason your AI adoption is stuck at 15%?
Your engineers don't trust the agents.
They've seen too many PRs where the agent built something technically correct but strategically wrong. They've spent too many hours debugging why the AI made certain assumptions.
So they limit AI to low-stakes tasks. Boilerplate. Utility functions. Things where mistakes are cheap.
You can't fix trust by buying better AI. You fix it by giving AI better inputs.
Complete specs → Clear implementation → AI agents that actually deliver → Engineers who trust and adopt.
Incomplete specs → Clarification loops → AI agents that generate rework → Engineers who opt out.
What Engineering Leaders Should Do This Week
If you're a CTO or VP watching your AI adoption ROI lag:
Audit one recent feature:
How many hours went to clarifications vs. actual coding?
What ambiguities in the spec caused rework?
If you'd used an AI agent, would it have made the right assumptions?
Run this calculation:
[# of engineers] × [hours per week on clarifications] × [hourly cost]
That's your monthly spec tax
Try this experiment:
Take your next PRD and ask: "What edge cases are missing?"
Count how many your PM can list vs. what gets caught in implementation
The gap is your AI agent failure rate
The Bottom Line
AI coding agents aren't going to magically make your team 40% more productive if you're feeding them incomplete specs.
The real unlock isn't better AI—it's treating specs as the interface to AI, the same way APIs became the interface to services.
Complete specs → Clear implementation → AI agents that actually deliver.
Incomplete specs → Clarification loops → AI agents that generate rework.
We built Lyra because we kept seeing engineering teams blame their AI tools when the real problem was upstream. If you're a CTO or VP of Engineering dealing with this, I'd love to show you what Lyra catches in your actual PRDs.
Get a free 30-minute spec audit this week. Bring one of your recent PRDs. We'll run it through Lyra live, show you every gap your team would have hit in implementation, and calculate your exact monthly spec tax.