Methodology & Process

Agent-Centric Development Cycle (ACDC): An AI-Native SDLC

Eastgate's own flavor of harness engineering

Eastgate Software Engineering

September 2026

Eastgate Software - German Engineering Standards. Enterprise-Grade Results.

Methodology & Process

Agent-Centric Development Cycle (ACDC): An AI-Native SDLC

Eastgate's own flavor of harness engineering

With AI, it is no secret that an engineer can write code much faster now. However, speed is only one thing, accuracy is a much different story. Lack of audit trail and ways to verify intent are just some of the common examples. The input was never precise enough to begin with, so teams iterate faster but end up reworking more.

Our answer is simple. With research and data from real clients and bold innovators, we turn AI-written code into something tighter, safer, sharper, and actually ready to ship.

Eastgate Software Engineering September 2026
Agent-Centric Development Cycle white paper cover

Why Are Teams Using AI Still Shipping Bad Code?

AI coding assistants changed how fast we write code. But they did not change how hard it is to write the right code. That said, the problem was actually the gap between what the business needs and what the developer understands.

When requirements live in chat history, AI produces unpredictable results. No audit trail, no shared understanding, no way to verify intent. Anthropic's own research on Claude Code usage found that even software professionals reach verified success, meaning passing tests and real commits, in only about 30% of their sessions.

Our answer is a lightweight specification layer between intent and implementation. Requirements are encoded with concrete examples before we write any code. AI agents build from those specs, a review agent enforces standards, and humans hold the approval gates. This method is what we call the Agent-Centric Development Cycle, or ACDC.

The original idea of ACDC

The underlying ideas come from an emerging industry pattern called harness engineering by Martin Fowler. Even OpenAI has been running it internally on their own Codex team. The same instinct shows up over a decade earlier, in Gojko Adzic's Specification by Example. It shows up again in OpenSpec, an open-source project from Fission AI that's picked up more than 33,000 GitHub stars.

We build our ACDC based on these fundamentals, along with practices and standards we've refined across years of delivery for Siemens Mobility, FinTech platforms, and enterprise SaaS clients.

What Are the Risks of AI Coding Without Clear Controls?

Independent benchmarks across 470 real GitHub pull requests and 211 million lines of production code show what AI-assisted development looks like when it runs without structure. This is AI without a harness.

1.7×

More issues per PR
(10.83 vs 6.45)

1.5–2×

More security vulnerabilities

More than 5 line copy
-paste blocks since 2022

3.1% → 5.7%

Code rewritten within 2
weeks of commit
(from 2020 to 2024)

Without a harness, code quality degrades, technical debt explodes, and security vulnerabilities multiply. The harness is what makes AI-generated code ready for production.

What Is an AI-Native SDLC, and Where Does ACDC Fit?

An AI-native SDLC gives people and AI agents a shared process for delivering software. It extends beyond coding to the decisions and checks that help each release meet business needs. In an agentic SDLC, these responsibilities stay connected throughout the project.

ACDC provides a foundation for this kind of agentic software development at Eastgate. Clear specifications guide agents with defined roles, while your team retains approval over key decisions. Together, these practices shift the focus from completing individual tasks faster to improving delivery as a whole. The table below shows how that shift changes the way your team works and measures success.

Dimension What Most Companies Do AI-native delivery at Eastgate
Process Add AI assistance to tasks within the existing workflow. Connect AI-supported work from requirements through release and production feedback.
Roles Use AI assistants within existing team roles. Give agents defined responsibilities and name the person accountable for each result.
Quality checks Apply existing review and testing practices to AI output. Check output against agreed specifications, with enforced checks before human approval.
Decisions Use AI recommendations to support decisions within the team. Record key trade-offs and assign a clear owner for approval.
Measures Track AI usage and time saved on individual tasks. Measure overall delivery time alongside rework and defects after release.
Collaboration Share AI outputs through standard team handoffs. Carry shared project context and review evidence between people and agents.

"Agent = Model + Harness. The harness is everything that supports the model's operation."

- Martin Fowler, ThoughtWorks

What Are ACDC's Five Layers?

ACDC combines five layers. Each answers a different question about the work, from who performs a task to who approves the result.

Layer 1

AI Models & Agents

Who performs the task? Agents take on defined work within an agreed scope. Verify the output, regardless of how confident the explanation sounds.

Layer 2

Harness Engineering

What guides and constrains the work? Supply relevant context and apply checks. Use access controls to limit the actions available to an agent.

Layer 3

Spec-Driven Development

What should the software do? Record the required behavior before implementation, so the specification states what the software must do instead of leaving an agent to infer it.

Layer 4

Test-Driven Development

What evidence will show it works? Write a test for the required behavior, confirm the expected failure, then implement. Review generated tests because they can repeat the code's mistaken assumptions.

Layer 5

Human-in-the-Loop

Who makes the decision? People approve intent and resolve important trade-offs. Match review depth to risk and assign a clear owner for release approval.

Together, the layers connect intent to evidence. They support disciplined delivery, but do not guarantee a defect-free release. Apply them to the practical needs of your project:

Reduce avoidable rework. Agree on behavior before implementation so reviewers have a shared basis for assessing the result.

Keep requirements portable. Plain-text specifications are easy to inspect and transfer. Moving tools may still require changes to hooks and integrations.

Make project context easier to find. Maintain a clear entry point to current requirements, architecture decisions, and conventions.

Connect each change to its purpose. Link the requirement to the code review and preserve the evidence behind approval.

Review documentation with the change. Agents can draft updates, while checks and reviewers confirm that the documentation reflects the shipped behavior.

Address recurring sources of debt. Use review findings to improve shared rules and tests, then check whether the same problems return.

Use parallel agents where work is independent. Agree on interfaces and responsibilities first. Increase concurrency only while the team can review the resulting work properly.

Why Is This Called Harness Engineering?

An AI agent is really two parts, a model and a harness. The model brings raw capability, the intelligence that can write code. The harness brings the structure around it. That means rules, constraints, and a way to check whether the result actually holds up.

Without a harness, AI produces unpredictable results. With one, it has clear blueprints to build from. So when something goes wrong, the fix is to find the missing guide and make it enforceable.

Industry signal

How OpenAI ships software with Codex agents

OpenAI publishes this exact discipline under the name "harness engineering," a metaphor borrowed from the art of horse tack, where reins, saddle, and bit channel this powerful animal.

>90%

of the Codex app's code was generated by Codex itself

4 to 8

parallel agents per engineer at any given time

~1M

lines shipped in 5 months with no manually written source

"Humans steer. Agents execute."

Sources: OpenAI: Harness engineering, How OpenAI uses Codex (PDF), Martin Fowler: Harness engineering for coding agent users.

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Harness engineering: input guidance and output verification

Input guidance

Give agents usable context

Agreed requirements with Given / When / Then examples

Current domain context and clear terminology

Architecture constraints and relevant coding standards

Task scope, expected outputs, and unresolved questions

Output verification

Require evidence before approval

Deterministic checks: linters, type checks, and tests

Judgment: agent review assessed against the specification

Human decisions: risk review and release approval

Required checks and permissions enforced in project tooling

Turn failures into better checks. Fix the defect, then examine why the process missed it. Update the requirement, test, or control and check it against the original failure. A longer instruction file alone does not demonstrate that the problem is solved.

Select a step to walk the loop

Four steps, then the cycle begins again.

Step 01 · Find the failure

Start with a specific failure. Choose a defect that reached production, a recurring review comment, or a passing test that missed incorrect behavior. Describe what went wrong and what should have happened instead.

Step 02 · Trace the missing check

Next, identify the missing control. Ask what should have caught the problem. Look for an unstated requirement or a test that checks the implementation without verifying the intended behavior. Use that finding to decide what the harness needs to catch.

Step 03 · Update the control

Then, update the relevant artifact. Correct the specification or test, and adjust shared rules or permissions where needed. Wherever possible, turn the rule into a check the pipeline can enforce, rather than relying on written instructions alone.

Step 04 · Verify the correction

Finally, verify the correction. Test the updated control against the original failure and confirm it catches the problem. Until then, the control remains unproven. Once it works, the loop is complete. Return to step 01 when the next failure appears.

The Six Stages of Eastgate's ACDC

The five layers describe what ACDC is built from. The six stages below show when each layer applies, as one change moves from a business request to production feedback. Each stage should leave a usable output for the next person or agent, and the exact setup depends on your systems and approval requirements.

  1. Stage 01

    Plan: Agree on the Business Outcome

    Start with the problem and who it affects. Describe what a successful change would achieve, then identify the limits on that change.

    An agent can help turn the discussion into a brief. The business owner checks that it reflects the intended outcome. Keep unresolved questions visible rather than allowing a polished draft to hide them. The business owner confirms the outcome before design starts.

    Output: Intent brief

  2. Stage 02

    Design: Make Requirements Testable

    Turn the approved outcome into behavior the team can check. Resolve access rules and important failure cases while changing direction is still inexpensive.

    The design review should also settle what is outside scope. For the order portal, reading an order's status does not automatically include permission to edit it. The team agrees on testable behavior before implementation begins.

    Output: Specification and design decisions

  3. Stage 03

    Build: Work From an Approved Plan

    Give implementation a defined scope and a clear order of work. If the approach changes, record why so the reviewer can assess the difference.

    Parallel agents are useful when tasks are sufficiently independent. Set responsibilities and agree on shared interfaces first. Increase concurrency only while the team can still review the resulting work properly. Engineers review the changes against the agreed scope.

    Output: Implementation plan and code diff

  4. Stage 04

    Test: Check Behavior Throughout Implementation

    Run relevant checks as the feature develops. A passing unit test does not establish that the complete user journey works, so include integration checks where systems meet.

    For the order portal, test permitted access and rejected access. Also check what the user sees when the order service is unavailable. These cases should come from the requirement, not only from the implementation. Reviewers check the evidence against the requirement.

    Output: Test results and unresolved findings

  5. Stage 05

    Deploy: Release With Evidence and a Recovery Plan

    The release decision needs more than an agent's completion message. Review the test results and unresolved findings, then confirm that the release meets the project's approval rules.

    Agree on the recovery path before deployment. Make sure the responsible person can see what changed and knows how to respond if the service behaves unexpectedly. The release owner approves the deployment.

    Output: Release decision and recovery plan

  6. Stage 06

    Maintain: Use Production Findings to Guide the Next Change

    Let service evidence inform the backlog. An agent may help summarize an incident or prepare a proposed fix, subject to the access it has been granted.

    Keep authority explicit. Permission to inspect an alert does not also grant permission to alter production. Review the proposed change through the project's normal controls and add a regression check where appropriate. The team reviews the proposed correction like any other change.

    Output: Incident record and follow-up change

How Do People and Agents Coordinate Daily Work?

The harness gives each agent a defined role and an expected output. Coordination keeps shared work aligned. Review checks the result against the requirement, while people remain accountable for decisions.

Coordination: give each role a clear responsibility

Shared reference Everything below builds from this one record.

Agreed record

Specification with concrete scenarios

An agreed reference for implementation and acceptance

Built in parallel File and interface ownership is agreed before work starts.

Agent

Backend engineer agent

Service changes and unit tests linked to the requirement

Agent

Frontend engineer agent

Interface changes with integration checks

Then in sequence Each hand-off carries its evidence forward.

Agent

Tester agent

Test evidence, coverage gaps, and regression checks

Agent

Code reviewer agent

Findings for resolution before human approval

Human decision An agent recommends. A person decides.

Person

Accountable approver

The decision to proceed, recorded with the evidence behind it

Underneath all of it

Shared rules, hooks, and context

Updated guidance and verified controls for recurring work

Review: challenge the result against the requirement

Give the reviewer the specification, the change, and the test results. Ask it to identify contradictions and missing evidence. An agent's "pass" is a recommendation; required project checks and human approval determine whether the change can proceed.

Business analyst Specification

Make acceptance criteria explicit and confirm intent with the business owner.

Engineer Implementation

Build from the agreed behavior and show how the tests check it.

Reviewer Decision evidence

Challenge gaps and surface findings for the responsible human approver.

Replace vague wording with observable behavior

Nothing here to test

"Show orders securely"

A reviewer can check this

Given a signed-in customer, when they request another account's order, the system rejects access and returns no order details.

Challenge these words in any specification

AppropriateUser-friendlySeamlessEfficientFast

How Should You Measure the Value of ACDC?

Measure ACDC across the full delivery cycle. More code or more open pull requests show increased activity, but they do not tell you whether customers receive better software sooner.

Before the pilot starts, select the measures that match your goals and record a baseline. Then compare similar work over a defined period. Read delivery outcomes alongside workflow health to see where progress creates pressure elsewhere. For example, faster implementation brings limited value if changes spend longer waiting for review.

For the Business: Delivery Outcomes

For Engineering: Workflow Health

Your pilot measurement plan

No measures selected yet.

Tick the measures you will hold the pilot to, then record what each one reads today. Saved in this browser only.

Copy the plan and paste it into your own document to keep it.

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What Does Eastgate's ACDC Harness Include?

Eastgate packages its approach in the EGS Engineering Kit, built around Claude Code and OpenSpec. It brings shared agent definitions and workflow guidance into projects so teams can work from a common starting point. The published kit inventory includes:

  • 7 AI Agents

    Defined responsibilities for planning, implementation, testing, and review. Each task should have an agreed scope and an expected output.

  • 11 Lifecycle Hooks

    Checks at configured workflow events. Command hooks can enforce deterministic rules; model-based checks still require judgment.

  • 13 Coding Rules

    Shared workflow conventions and standards for the relevant technology stack. Rules guide work; required checks verify compliance.

  • 60+ Role Skills

    Guidance for recurring work across business analysis, engineering, QA, DevSecOps, project management, and pre-sales.

  • 24 Knowledge Packs

    Reference material for specialist tasks, including architecture, security, and nonfunctional requirements.

Put the kit in context. Start with the delivery problem you want to solve. Agree on the relevant controls, the authoritative project records, and how success will be assessed. Installation is the starting point; the team still defines how roles collaborate and who approves the result.

Frequently Asked Questions

What Is the Agent-Centric Development Cycle? +

ACDC is Eastgate's software delivery methodology built around structured specifications, AI agents, and human approval. It connects implementation to requirements and review evidence.

Is ACDC an AI-Native SDLC? +

ACDC provides a foundation for an AI-native SDLC through shared specifications and controlled agent workflows. Its reach across the lifecycle depends on the processes configured for the project.

How Does ACDC Differ From AI Coding Tools? +

AI coding tools provide capabilities for producing and changing software. ACDC organizes how a team uses those capabilities, including requirements, checks, and responsibility for decisions.

What Is the Difference Between AI-Assisted and AI-Native Development? +

AI-assisted development adds AI support to tasks within an existing workflow. AI-native development redesigns the workflow around agent capabilities and the controls needed to use them responsibly.

Is an Agentic SDLC the Same as an AI-Native SDLC? +

The terms often describe overlapping approaches to using agents across software delivery. Assess the actual workflow and approval rules when comparing implementations.

Does ACDC Support Roles Beyond Developers? +

Yes. Eastgate's role pipelines also cover business analysis, QA, DevSecOps, project management, pre-sales, and solution architecture.

Does ACDC Replace Agile? +

No. Teams can keep their Agile practices while adding clearer specifications and checks for work performed by agents.

Does Spec-Driven Development Slow Down Small Changes? +

A full specification can add unnecessary overhead to a small, well-understood fix. Match the documentation to the change, while retaining the tests and approvals it needs.

What Is OpenSpec? +

OpenSpec is an open-source framework for spec-driven development with AI coding assistants. It helps teams maintain requirements and proposed changes alongside their code.

How Does Human-in-the-Loop Approval Work? +

People review the intended behavior and approve changes at the project's defined gates. Agents may perform approved tasks within scope, but cannot grant themselves broader authority.

Can ACDC Work With Existing Delivery Tools? +

Yes, ACDC can be applied alongside existing version control, CI/CD, and project management tools. The project must define where authoritative records live and how they connect.

Does ACDC Remove Vendor Lock-In? +

Plain-text specifications and project files make the methodology easier to inspect and transfer. Moving to another tool may still require changes to hooks, permissions, and integrations.

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About Eastgate Software

Eastgate Software is a strategic engineering partner headquartered in Hanoi, Vietnam, with offices in Aachen, Germany and Tokyo, Japan. With 200+ engineers, 93% team retention, and 12+ years of delivery excellence, we build mission-critical systems for clients including Siemens Mobility and Yunex Traffic.

Our ACDC (Agent-Centric Development Cycle) methodology combines German engineering discipline with Vietnamese engineering talent to deliver enterprise-grade results across Intelligent Transportation, FinTech, Retail, and Manufacturing.

Contact: [email protected] | (+84) 246.276.3566 | eastgate-software.com

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