AI agents promise to eliminate toil and accelerate developer productivity - but most never make it past the demo. The gap isn't technical capability; it's missing organizational context, embedded standards, and proper governance. This post breaks down how platform teams can build AI agents that understand your stack, follow your conventions, and scale beyond the platform team.

Main insights

• Agent sprawl happens when teams build without unified context, standards, or governance - turning platform teams into bottlenecks instead of enablers.
• Production-ready agents require five things working together: unified context, scoped permissions, embedded judgment, human-in-the-loop review at the right stage, and centralized management.
• Vibe coding with organizational context lets any team build platform capabilities while maintaining standards and control - but the context layer, not the prompt, determines output quality.

John Crowley, Technical Product Marketing Manager at Port, and Roi Fine, Growth Product Manager at Port, walked through how platform teams can enable AI agent development across their organization without creating chaos. John brings experience from cybersecurity and defense software; Roi works at the intersection of product, data, and go-to-market strategy. Together, they offer a grounded, practitioner-focused perspective on what it actually takes to move agents from demo to production.

You can watch the full discussion here if you missed it.

The agent sprawl problem: When demos don't reach production

Organizations are moving fast on their AI journey. They started with DevOps, productized those efforts through platform engineering, and adopted coding assistants like GitHub Copilot and Claude. Now they're experimenting with AI agents for incident management, security triage, and infrastructure updates. The goal is clear: fully autonomous, AI-native engineering.

But something breaks in the middle. As John explained, the pattern repeats across organizations: "The demo works, everybody is really impressed, and then the agent takes four months to reach production or it just never gets there."

The problem isn't the agent's capability - it's missing information. "The agent doesn't have your standards," John noted. "Which compliance checks to apply, who has to approve, which repo - that isn't in the model that you're using to build that agent. It's really in your heads and in your wiki."

The stakes are real. DLO, a Port customer, demonstrates what's possible when you get this right: they're using agents to auto-resolve 45% of engineering tasks across their SDLC and have seen a 20% boost in developer performance. Results like these explain why demand for agents keeps growing - and why the pressure on platform teams keeps mounting.

Five requirements for production-ready agents

Requirement 1: Unify the context

Most people hear "context" and think retrieval. But context is everything your organization knows that the agent doesn't - what exists, who owns it, your team structure, integrations, dependencies, and critically, your standards.

"Release best practices, development guidelines, org standards, approval requirements - all that information that is living within your team's head," John explained. Connecting an agent to your systems is largely an authentication problem, and that's mostly solved. But whether the agent acts in line with how your org actually releases software is a different challenge entirely. "An agent can have perfect credentials and still ship something that violates every release convention that you have because nobody ever told it what those were."

Well-architected context also improves accuracy and cuts token costs. "If you stop shipping your entire stack into every prompt, that's real money at scale," John added.

Requirement 2: Define what it's allowed to do

Most teams build the agent first, then add guardrails second. That order is exactly why so many agents stall between demo and production.

The issue is review load. "If your only control is a human reading the agent's output, the system scales exactly as far as that reviewer's attention," John said. "Ten agent actions a week is fine, but scale that to 400 and review becomes rubber stamping."

Control must move upstream - into the path the agent can take before it acts. John distilled this into four questions every platform team must answer:

• What does the agent know before it acts?
• What can it touch, and with whose access?
• What rules must it follow, and can it improvise around them?
• When something goes wrong, who approved it and can you reconstruct it?

"You already enforce most of this on your human engineering teams," John noted. "But the issue is that guardrails get written after the agent already has credentials, whereas guardrails should be scoped, standardized, approved, and logged before the agent ever touches production."

Requirement 3: Build in judgment

Judgment must be built in, not prompted for. Your platform engineering expertise and conventions need to live somewhere more durable than a prompt someone typed that morning.

"On the platform side, the prompting works fine - you know platform conventions, you're an expert," John explained. "But it fails the moment a back-end team tries to build their own agent to accomplish a platform task, which is the entire reason you're trying to implement agents and open up this ecosystem to other teams."

The cost shows up as review load. Whatever judgment the agent lacks, a human supplies on the other end. As John put it: "If you're shipping agents with no embedded expertise, you ultimately get intern-level work at senior speed."

Requirement 4: Keep humans in the loop - but in the right place

Where you place humans in the loop determines whether you get meaningful oversight or a bottleneck.

Review before execution is critical. "The cheapest place to say no is while the agent still has a plan and no commits," John said. People who inherit finished work from an agent approve it far more often than people who review intent upfront.

Make the plan a concrete artifact - readable, specific, rejectable, and logged. A chat message asking "should I proceed?" isn't a real review. Gate by risk level so you're not rubber-stamping low-stakes actions, and route approvals to the correct owner using your ownership context from requirement one.

"It's really the difference between a human gate and a queue that ends up in the platform engineering team's backlog," John explained.

Requirement 5: Maintain control through agent management

You need centralized visibility while remaining agent-agnostic. Teams adopt whatever their vendors ship - Bedrock comes with AWS, Copilot with GitHub, Cursor with developer laptops. "The requirement is neutrality, not consolidation," John said.

Produce reusable catalog items - workflows, dashboards, skills - that agents can use and trigger across your org. And critically: "You really can't govern what you can't see. Most orgs can't answer how many agents are running, who owns them, what they can touch, or what they changed last week."

Port AI Builder: Vibe coding with organizational context

Port built AI Builder as a vibe coding experience for creating platform capabilities using natural language while maintaining production standards. It comes with built-in platform engineering expertise. "It thinks like your most senior platform engineer," John explained. "It's going to know security, DevOps best practices, pipeline design, architecture, data modeling and of course Port's own constructs."

AI Builder sits on top of Port's context lake - a unified layer that takes into account your stack, policies, RBAC, existing services, and the relationships between them. When someone prompts for an agent to autonomously resolve incidents, Builder already knows they're talking about production incidents and understands the connected tools like PagerDuty.

Crucially, Builder isn't fire-and-forget. It generates a reviewable plan that humans can iterate on before anything is built. After approval, it moves into build mode in a versioned, traceable way. Builder can also invoke agents that already exist in your org, reducing sprawl and making it easier to manage what's actually running in your stack.

Live demonstration: From prompt to production workflow

Roi demonstrated three practical use cases showing AI Builder in action.

Creating a skills registry: Skills are reusable instruction sets that load automatically into AI agents as context. A simple prompt creates the registry; Builder asks clarifying questions, presents a plan with an architecture diagram, and after approval, builds the registry - pulling in all organizational skills with descriptions, owning teams, and groupings. A follow-up prompt generated a dashboard with widgets showing total skills, invocations, and usage by team.

Connecting a new data source: Adding Jira as a data source used to require significant manual configuration. Telling Builder "I want to connect Jira" triggers clarifying questions, then routes directly to Jira's authentication page. After selecting the site, Jira appears in the context lake with data sources updated automatically.

Building an agentic workflow: The most sophisticated demo showed creating an agentic PRD (Product Requirements Document) workflow. The workflow takes a newly created ticket, runs a PRD agent to review and improve the description, then follows additional steps. Builder generated a multi-step workflow with configurable triggers, an agent run using the PRD skill, a suggestion step, and a human review gate. At the review gate, Roi compared the original lean description with the AI-suggested version based on organizational standards - and approved it only after confirming it met requirements. "You have your own requirements how a description should look like," Roi explained. "Once I like it, I can let it know looks great and the ticket would be approved."

Governance and enterprise readiness

When asked how Port differs from workflow tools like n8n, Roi emphasized governance. "We have a lot of governance features baked in. You can define a certain threshold and rules that each workflow must pass before it can actually be run."

Port provides audit logs for each agent run and workflow, making it suitable for enterprises that need to scale agentic workflows while maintaining control. The platform also supports bringing your own LLM models - AI Builder ships with Anthropic's model out of the box, but organizations can connect custom AI models specific to their needs.

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Key takeaways

• Agent sprawl is an organizational problem, not a technical one. Without unified context, embedded standards, and proper governance, every team building agents creates more work for the platform team instead of reducing it. Making organizational knowledge accessible to agents before they act is the foundational fix.
• Production readiness requires five elements working together. Unified context, scoped permissions, embedded judgment, human-in-the-loop review at the right stage, and centralized management aren't optional features - they're requirements. Missing any one creates bottlenecks or unacceptable risk.
• The context layer - not the prompt - determines output quality. Natural language prompting produces intern-level work at senior speed unless the agent has access to your actual standards, ownership model, and conventions. Vibe coding only works when it sits on real organizational context.
• Review the plan, not the output. The cheapest place to say no is while the agent still has a plan and no commits. Reviewing finished work leads to rubber-stamping. Reviewing intent with readable, specific, rejectable plans keeps humans meaningfully in the loop without turning them into bottlenecks.