Agentic AI Development Framework: Product & Engineering Guide

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A shared framework for scoping agentic AI when product wants results and engineering wants guardrails

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Intro: When AI systems are fragile, collaboration isn't optional

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If you're building with AI, you've probably heard someone say, "What we really want is something agentic."

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Sometimes that means "a smart assistant that gets things done."

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Sometimes it means "an automated workflow that requires zero input."

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Sometimes it means "we don't totally know, but it should be impressive."

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The problem isn't ambition. It's ambiguity.

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Without a shared way to scope complexity, product teams design for trust and usability while engineering teams try to contain risk and avoid catastrophic failure. Both sides are right—but without a common frame, the work gets slow, brittle, and expensive.

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So here's a model we use to cut through the noise.

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Agentic Behavior in Practice: Five Patterns We Actually See

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We're not claiming these are universal levels. We're saying these are the five patterns that come up again and again in the field—regardless of what your team calls them.

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If you mean...	What you're building	What to expect
"It runs a task when I ask"	Single-step trigger	Fast to implement, easy to trust
"It chooses a tool to use based on the request"	Tool selection logic	Still bounded, needs good fallback
"It can follow a multi-step process"	Sequenced workflow	Fragile if accuracy or handoff is unclear
"It figures out what to do and how to do it"	Self-directed agent	High risk, often unstable
"It acts on its own without being asked"	Autonomous initiator	Not real (yet), not shippable

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This isn't about dumbing things down. It's about giving cross-functional teams a shared mental model so no one is designing for something that can't be built—or building something no one actually wants to use.

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From the Product Side: What You Should Be Asking

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What you're building	What to ask	What to design for
Single-step trigger	What's the clearest, most valuable task this can automate?	Simplicity, speed, clarity
Tool selection logic	How will users know what's happening behind the scenes?	Confidence thresholds, user control
Sequenced workflow	What's the trust floor? Where does the human step in?	Review workflows, fallbacks
Self-directed agent	What's our risk tolerance? What happens when it fails silently?	Expectation setting, system visibility
Autonomous initiator	Why do we want this? What else could we test instead?	Controlled pilot, minimal blast radius

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From the Engineering Side: What You Need to Build for

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What you're building	What to engineer	Risks to watch
Single-step trigger	One API call or tool execution	Minor if isolated
Tool selection logic	Tool routing, parameter handling, structured outputs	Tool misuse, unexpected results
Sequenced workflow	State management, restart logic, traceability	Compounding error, poor handoff
Self-directed agent	Dynamic planning, sandboxing, override hooks	Unpredictable chains, legal exposure
Autonomous initiator	Event detection, continual context, scheduling	Zero visibility, irrecoverable failures

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When You're Working Together

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What you're building	Align on	Risk posture	Viable starting point
Single-step trigger	Clear user value	Low	Automate a known, frequent action
Tool selection logic	Output transparency	Medium	Let users approve or edit output
Sequenced workflow	Human handoff points	Medium–high	Start with structured cases only
Self-directed agent	Acceptable error ceiling	High	Pilot with internal testers
Autonomous initiator	Why now?	Critical	Don't. Fund R&D instead.

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Want to try this live? We've been building out a Miro-based template version for real-time scoping. Reach out if you'd like access.

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Final Thought

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Agentic AI isn't just a modeling challenge. It's a collaboration challenge.

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Product wants to build trust. Engineering wants to avoid disaster. If you align on what you're really trying to ship—and what it will take to keep it stable—you can move faster without breaking everything.

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Rebecca Loar

VP of Product

Rebecca leads Invene’s product strategy practice, helping healthcare clients de-risk big decisions and bring scalable, market-ready solutions to life. She specializes in aligning complex systems to real-world workflows and guiding teams through product–market fit and go-to-market strategy. Her earlier strategic roles include executive advisory programs at Rackspace, global HIV-prevention work with the Gates Foundation, and upstream research for Procter & Gamble and ConAgra. She is also a strategic advisor and co-founder of Femovate, a femtech accelerator behind more than two dozen regulated women’s health products. Outside of work, she does construction work on her rural Texas property, reads medical coding textbooks for fun, and tries to vaccinate the local feral cat population.

Brian Kamras

Senior Backend Developer

Brian is a creative full-stack engineer specializing in AI and large language model (LLM) research and engineering with a strong foundation in web-based and IoT product development.

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