The Agentic SDLC: Turning AI from Tools into Teammates
“The future of software development isn’t AI-assisted. It’s outcome-governed.”
— Scott Silvi, Founder & Chief Engineer, BlueFolders
Why We Built This Demo
Every week brings a new wave of headlines about the “AI bubble.”
Some say it’s bursting. Some say it’s just beginning.
Here’s the truth: the financial bubble may be correcting - but the technology is compounding.
Models are getting faster. Agents are getting smarter. Integrations are getting tighter.
The problem isn’t the tech.
It’s the systems we keep trying to bolt it onto.
At BlueFolders, we see this everywhere: brittle handoffs, manual approvals, shadow spreadsheets, and workflows that were never designed for agents. It’s why 95% of agents fail before reaching production - not because they can’t reason, but because there’s no governance, observability, or trust framework to support them.
That’s what this demo sets out to show:
👉 What happens when we replace those brittle handoffs with governed, agentic workflows.
👉 How OutcomeOS™ - our agentic operating system - transforms AI from tools into teammates.
The Big Idea: The Agentic SDLC
Traditional software development is a relay race.
Product writes specs → Engineering implements → QA tests → Ops deploys.
Every handoff bleeds context. Every dependency adds delay.
And somewhere along the line, someone forgets why the feature existed in the first place.
The Agentic SDLC flips that.
Instead of human handoffs, agents carry context across the entire lifecycle - from PRD to implementation to deployment - while humans stay in the loop to review, approve, and govern outcomes.
It's a simple but radical shift:
This isn't about replacing engineers.
It’s about removing friction - and letting humans focus on judgment instead of translation.
Demo Recap: From Idea to Implementation
Engineered & demoed by: Adam Terlson (CTO, BlueFolders)
The demo uses Navion, one of our portfolio companies in logistics and transportation, as a proving ground.
In Navion, browser-based AI agents already automate data collection from hundreds of fuel tanks nationwide. But this time, we asked the system to invent a new tool it didn’t yet have - fully autonomously - inside a live product environment.
Here’s what happened:
- 🔹Adam asked the chatbot to query “active alarms” at a specific site.
- 🔹The system realized it had no existing tool for that request.
- 🔹It launched a new agentic workflow to specify, review, build, and deploy that new capability - in real time.
- 🔹A human (Scott) approved the PRD mid-flight.
- 🔹The workflow passed to Claude Code, which implemented the logic.
- 🔹Temporal orchestrated the durable execution pipeline, ensuring each step was tracked, auditable, and recoverable.
- 🔹Within moments, the new tool was live - returning the requested alarm data.
No tickets. No manual handoffs.
Just a governed, observable, human-approved workflow running end-to-end.
Why It Matters
Agentic workflows don’t just make software faster.
They make it trustworthy.
- 🔹Every action is logged, auditable, and reversible.
- 🔹Every agent operates inside a policy boundary.
- 🔹Every approval step can be human-in-the-loop (HITL) or human-on-the-loop (HOTL).
It’s the OutcomeOS principle in motion:
Humans define intent, AI executes the workflow, governance ensures safety, and receipts prove the outcome.
That’s how AI moves from hype to production.
Q&A Highlights
Edited from the live demo discussion with Azeez “Ozzy” Hayne (CEO, Phindyr), Eric Edwards (Chief Strategy Officer, BlueFolders), and Zach Lendon (Chief Product Officer, BlueFolders).
Q1 - How has OutcomeOS evolved since those early builds?
Ozzy: You used an early version of OutcomeOS when you built Phindyr with us about six months ago. How has the process changed since then?
Scott: The early version was much more manual. Our team used prompt templates to generate PRDs, run gap analyses, detect conflicts - all by hand. Then we’d push everything to GitHub and manually trigger coding agents.
The biggest leap since then is end-to-end orchestration. Each step now lives inside a governed workflow with clear policy gates.
PRDs must be approved before implementation. Once approved, they move automatically through build, test, and deployment - no copy-paste loops, no friction. We can even enforce double approvals for higher-risk actions like adding new tools.
Q2 - Are the tools themselves improving as you go?
Ozzy: You’ve said before that this process doesn’t just level up people - it levels up the tools. How do you see that compounding?
Scott: Exactly that. Think of it as a continuous feedback loop between tools and process.
Some steps use best-in-class platforms like Claude Code or Codex; others integrate third-party HOTL/HITL layers. We build the glue around them so the entire SDLC becomes orchestrated, observable, and adaptive.
And it doesn’t have to be all-or-nothing - teams can start slice by slice, automating one part of the lifecycle at a time.
Q3 - How confident are you in what the system builds?
Eric: Confidence starts low and grows with experience. Where are you on that curve now, and how do you measure it?
Scott: Confidence varies by function. Some steps are near-autonomous; others still require human oversight.
We’re constantly building confidence through what we call the proof layer - our internal “Receipt Studio.” Every agent action logs its metadata: inputs, outputs, token usage, even traces.
Those receipts let us evaluate, audit, and rollback automatically if something drifts. Over time, as those proofs accumulate, confidence grows - and delegation to agents increases.
Q4 - When does this move beyond internal use?
Eric: Right now this shortens our own SDLC. How far are we from deploying this with customers?
Scott: Even externally, humans stay in the loop.
Zach: Enterprises already have strong governance - identity, policy, and compliance layers. We meet them there. OutcomeOS provides the guardrails, runtime observability, and drift detection that make it safe to bring agentic workflows into production.
It’s not about replacing enterprise tooling - it’s about orchestrating trust across it.
The Road Ahead
OutcomeOS isn’t a product demo.
It’s a philosophy of how we’ll build, scale, and govern the next generation of AI-native companies.
Humans will always define the “why.”
Agents will handle the “how.”
And capital - disciplined, outcome-backed capital - will fuel it all.
That’s the future we’re building towards.
📹 Watch the full demo below
Related Reading
- 🔹Spec Debt: The Costliest New Tech Debt in the Age of AI Agents
- 🔹OutcomeOS: Our Agentic Operating System for Building AI-Native Companies
BlueFolders
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