Kiro: AI spec-driven delivery and what it means for the future of software development
The rapid evolution of AI in software engineering has produced no shortage of coding assistants, copilots and experimentation tools. Many promise productivity gains, but few fundamentally reshape how software is designed, governed and delivered.
Kiro, which was a main feature of AWS re:Invent, represents a more meaningful shift. Rather than accelerating individual coding tasks, Kiro introduces a native spec-driven, agentic approach to software development that has the potential to materially change delivery models, team structures and commercial outcomes across the industry.
For organisations looking beyond experimentation and toward production-grade impact, Kiro raises both significant opportunities and important strategic challenges.
Kiro is an AI-enhanced development platform (IDE and CLI) designed to support end-to-end, spec-driven software delivery. Unlike traditional coding assistants that are not native spec-driven first, or mainly focuson inline suggestions or code completion, Kiro uses intelligent agents to help teams:
In practical terms, developers can describe features or fixes in plain English – for example, structured user stories using formats such as EARS – and Kiro produces complete, testable and production-ready outputs aligned to those specifications.
Kiro also supports native steering docs, letting us define our own coding patterns, standards, approaches, and tooling so the system aligns with our organisational practices. This approach shifts AI-assisted development toward repeatable, auditable and business-aligned delivery, positioning AI as a team member as opposed to simply a tool.
Several Kiro capabilities are particularly significant from an enterprise and commercial perspective.
The focus on spec-driven development at scale enforces a discipline where specifications and plans are created before code. This improves alignment between business intent and technical output, reducing re-work and misinterpretation.
In addition, through the creation of steering documents, which outline how we build things and what frameworks and patterns we use, we can guide the model in building things in a consistent way.
Kiro generates integrated code, tests and documentation including application code, automated tests and supporting documentation (e.g. Markdown, Mermaid diagrams, API definitions). This also offers an additional level of consistency across the development cycle and improves maintainability, while also supporting governance, onboarding and compliance requirements.
Agent hooks and workflow automation allows teams to automate repetitive tasks, for example, automatically updating OpenAPI specs when endpoints change, enforcing architectural or coding standards and keeping documentation in sync with implementation, helping organisations maintain quality at speed as systems evolve.
Finally, perhaps one of the most interesting features of Kiro is the ability for teams to interact with the platform as a collaborative “team member” allowing them to ask questions, explore design options, debug issues, refactor components and integrate in real-time. This new approach paves the way for true integration into the development cycle, complementing skilled developers by supporting them with repetitive, time-consuming tasks and freeing up resource for more complex parts of the development cycle.
While Kiro is a technical platform, the implications it – and other platforms like it - have for businesses both commercially and strategically are vast. At Leighton our early experience with AI-assisted spec-driven development suggests material productivity gains – with our AWS Practice estimating up to a 6x increase in output and delivery speed when used effectively. Our time is then spent refining plans, updating steering docs, incrementally reviewing code as tasks are competed, and planning the next iteration of changes.
The system can be used to facilitate faster prototyping and validation with customers by allowing iteration in real time. This in turn shortens feedback loops and provides a quicker path from concept to minimum viable product (MVP). For businesses operating in competitive or time-sensitive markets, this can be a decisive advantage.
By automating manual and repetitive development tasks, Kiro allows organisations to improve resource efficiency – re-allocating skilled engineers to higher-value activities and reducing bottlenecks caused by limited specialist capacity. This has clear implications for cost control, scalability and delivery confidence.
Spec-driven delivery, combined with automated testing and documentation, can also improve consistency across teams and projects, alignment with architectural standards and the quality and maintainability of codebases. Overtime, this can reduce maintenance costs, incident rates and long-term technical debt.
Despite its promise, AWS Kiro is not a plug-and-play productivity tool. Its success depends on deliberate organisational change.
Implemented well, Kiro can shift the emphasis for development teams from manual coding to high-quality specification and design, architecture, integration and security oversight and continuous review and validation of AI-generated outputs. This naturally means senior developers will be essential to successful implementation.
Organisations wishing to use these tools effectively must re-think their entire approach to the software development lifecycle especially team structures, workflows and performance measures. In order to harness the full power of AI and its role in the work we deliver, companies need to avoid simply overlaying AI onto outdated delivery models which can exacerbate existing inefficiencies. In addition, to use Kiro effectively, teams need strong product skills, experience in prompt engineering and AI steering and senior engineers capable of validating and guiding AI output.
This might drive organisations towards smaller teams with greater depth of experience, rather than larger teams focused on manual execution. However, one of the most significant long-term risks is over-reliance on AI tools. The oversight and management by senior developers will remain essential and so junior engineers still need to learn how to design and write software manually to ensure the right talent in the future. Without these foundational skills, they cannot validate AI-generated code and organisations risk future skills shortages if training is neglected. This makes a commitment to structured learning pathways and deliberate skills development fundamental for companies.
Beyond its impact on the workforce, there is also a complex governance, compliance and IP landscape to be navigated. Automated code and documentation raise important questions around intellectual property ownership, regulatory compliance, auditability and accountability and security and data handling.
These considerations must be addressed within legal, risk and governance frameworks – before deployment.
Using AI-powered tools to assist with spec-driven development presents some huge opportunities for our industry. The organisations that succeed in maximising on those opportunities will be those that embrace spec-driven development as a business discipline, not just a technical one, and assess how AI can support that discipline in a structured way. In addition, those that invest in senior capability, governance and foundation training for junior team members to balance automation with human oversight and accountability will likely see more success. The way to maximise the impact systems like Kiro have at an operational level will be by treating AI as a collaborative team member, not a replacement.
For leaders, the question is no longer whether AI will impact software delivery but how quickly and how deliberately they adapt their operating models to capture value while managing risk.
At Leighton, we see AI-assisted spec driven development as catalysts for meaningful change – but only when paired with the right strategy, capability and commercial mindset. If you’re considering embedding Kiro – or a similar tool – across your organisation we’re here to help. Contact our AWS Practice and they’ll be happy to help!
The rapid evolution of AI in software engineering has produced no shortage of coding assistants, copilots and experimentation tools. Many promise productivity gains, but few fundamentally reshape how software is designed, governed and delivered.
Kiro, which was a main feature of AWS re:Invent, represents a more meaningful shift. Rather than accelerating individual coding tasks, Kiro introduces a native spec-driven, agentic approach to software development that has the potential to materially change delivery models, team structures and commercial outcomes across the industry.
For organisations looking beyond experimentation and toward production-grade impact, Kiro raises both significant opportunities and important strategic challenges.
Kiro is an AI-enhanced development platform (IDE and CLI) designed to support end-to-end, spec-driven software delivery. Unlike traditional coding assistants that are not native spec-driven first, or mainly focuson inline suggestions or code completion, Kiro uses intelligent agents to help teams:
In practical terms, developers can describe features or fixes in plain English – for example, structured user stories using formats such as EARS – and Kiro produces complete, testable and production-ready outputs aligned to those specifications.
Kiro also supports native steering docs, letting us define our own coding patterns, standards, approaches, and tooling so the system aligns with our organisational practices. This approach shifts AI-assisted development toward repeatable, auditable and business-aligned delivery, positioning AI as a team member as opposed to simply a tool.
Several Kiro capabilities are particularly significant from an enterprise and commercial perspective.
The focus on spec-driven development at scale enforces a discipline where specifications and plans are created before code. This improves alignment between business intent and technical output, reducing re-work and misinterpretation.
In addition, through the creation of steering documents, which outline how we build things and what frameworks and patterns we use, we can guide the model in building things in a consistent way.
Kiro generates integrated code, tests and documentation including application code, automated tests and supporting documentation (e.g. Markdown, Mermaid diagrams, API definitions). This also offers an additional level of consistency across the development cycle and improves maintainability, while also supporting governance, onboarding and compliance requirements.
Agent hooks and workflow automation allows teams to automate repetitive tasks, for example, automatically updating OpenAPI specs when endpoints change, enforcing architectural or coding standards and keeping documentation in sync with implementation, helping organisations maintain quality at speed as systems evolve.
Finally, perhaps one of the most interesting features of Kiro is the ability for teams to interact with the platform as a collaborative “team member” allowing them to ask questions, explore design options, debug issues, refactor components and integrate in real-time. This new approach paves the way for true integration into the development cycle, complementing skilled developers by supporting them with repetitive, time-consuming tasks and freeing up resource for more complex parts of the development cycle.
While Kiro is a technical platform, the implications it – and other platforms like it - have for businesses both commercially and strategically are vast. At Leighton our early experience with AI-assisted spec-driven development suggests material productivity gains – with our AWS Practice estimating up to a 6x increase in output and delivery speed when used effectively. Our time is then spent refining plans, updating steering docs, incrementally reviewing code as tasks are competed, and planning the next iteration of changes.
The system can be used to facilitate faster prototyping and validation with customers by allowing iteration in real time. This in turn shortens feedback loops and provides a quicker path from concept to minimum viable product (MVP). For businesses operating in competitive or time-sensitive markets, this can be a decisive advantage.
By automating manual and repetitive development tasks, Kiro allows organisations to improve resource efficiency – re-allocating skilled engineers to higher-value activities and reducing bottlenecks caused by limited specialist capacity. This has clear implications for cost control, scalability and delivery confidence.
Spec-driven delivery, combined with automated testing and documentation, can also improve consistency across teams and projects, alignment with architectural standards and the quality and maintainability of codebases. Overtime, this can reduce maintenance costs, incident rates and long-term technical debt.
Despite its promise, AWS Kiro is not a plug-and-play productivity tool. Its success depends on deliberate organisational change.
Implemented well, Kiro can shift the emphasis for development teams from manual coding to high-quality specification and design, architecture, integration and security oversight and continuous review and validation of AI-generated outputs. This naturally means senior developers will be essential to successful implementation.
Organisations wishing to use these tools effectively must re-think their entire approach to the software development lifecycle especially team structures, workflows and performance measures. In order to harness the full power of AI and its role in the work we deliver, companies need to avoid simply overlaying AI onto outdated delivery models which can exacerbate existing inefficiencies. In addition, to use Kiro effectively, teams need strong product skills, experience in prompt engineering and AI steering and senior engineers capable of validating and guiding AI output.
This might drive organisations towards smaller teams with greater depth of experience, rather than larger teams focused on manual execution. However, one of the most significant long-term risks is over-reliance on AI tools. The oversight and management by senior developers will remain essential and so junior engineers still need to learn how to design and write software manually to ensure the right talent in the future. Without these foundational skills, they cannot validate AI-generated code and organisations risk future skills shortages if training is neglected. This makes a commitment to structured learning pathways and deliberate skills development fundamental for companies.
Beyond its impact on the workforce, there is also a complex governance, compliance and IP landscape to be navigated. Automated code and documentation raise important questions around intellectual property ownership, regulatory compliance, auditability and accountability and security and data handling.
These considerations must be addressed within legal, risk and governance frameworks – before deployment.
Using AI-powered tools to assist with spec-driven development presents some huge opportunities for our industry. The organisations that succeed in maximising on those opportunities will be those that embrace spec-driven development as a business discipline, not just a technical one, and assess how AI can support that discipline in a structured way. In addition, those that invest in senior capability, governance and foundation training for junior team members to balance automation with human oversight and accountability will likely see more success. The way to maximise the impact systems like Kiro have at an operational level will be by treating AI as a collaborative team member, not a replacement.
For leaders, the question is no longer whether AI will impact software delivery but how quickly and how deliberately they adapt their operating models to capture value while managing risk.
At Leighton, we see AI-assisted spec driven development as catalysts for meaningful change – but only when paired with the right strategy, capability and commercial mindset. If you’re considering embedding Kiro – or a similar tool – across your organisation we’re here to help. Contact our AWS Practice and they’ll be happy to help!
The rapid evolution of AI in software engineering has produced no shortage of coding assistants, copilots and experimentation tools. Many promise productivity gains, but few fundamentally reshape how software is designed, governed and delivered.
Kiro, which was a main feature of AWS re:Invent, represents a more meaningful shift. Rather than accelerating individual coding tasks, Kiro introduces a native spec-driven, agentic approach to software development that has the potential to materially change delivery models, team structures and commercial outcomes across the industry.
For organisations looking beyond experimentation and toward production-grade impact, Kiro raises both significant opportunities and important strategic challenges.
Kiro is an AI-enhanced development platform (IDE and CLI) designed to support end-to-end, spec-driven software delivery. Unlike traditional coding assistants that are not native spec-driven first, or mainly focuson inline suggestions or code completion, Kiro uses intelligent agents to help teams:
In practical terms, developers can describe features or fixes in plain English – for example, structured user stories using formats such as EARS – and Kiro produces complete, testable and production-ready outputs aligned to those specifications.
Kiro also supports native steering docs, letting us define our own coding patterns, standards, approaches, and tooling so the system aligns with our organisational practices. This approach shifts AI-assisted development toward repeatable, auditable and business-aligned delivery, positioning AI as a team member as opposed to simply a tool.
Several Kiro capabilities are particularly significant from an enterprise and commercial perspective.
The focus on spec-driven development at scale enforces a discipline where specifications and plans are created before code. This improves alignment between business intent and technical output, reducing re-work and misinterpretation.
In addition, through the creation of steering documents, which outline how we build things and what frameworks and patterns we use, we can guide the model in building things in a consistent way.
Kiro generates integrated code, tests and documentation including application code, automated tests and supporting documentation (e.g. Markdown, Mermaid diagrams, API definitions). This also offers an additional level of consistency across the development cycle and improves maintainability, while also supporting governance, onboarding and compliance requirements.
Agent hooks and workflow automation allows teams to automate repetitive tasks, for example, automatically updating OpenAPI specs when endpoints change, enforcing architectural or coding standards and keeping documentation in sync with implementation, helping organisations maintain quality at speed as systems evolve.
Finally, perhaps one of the most interesting features of Kiro is the ability for teams to interact with the platform as a collaborative “team member” allowing them to ask questions, explore design options, debug issues, refactor components and integrate in real-time. This new approach paves the way for true integration into the development cycle, complementing skilled developers by supporting them with repetitive, time-consuming tasks and freeing up resource for more complex parts of the development cycle.
While Kiro is a technical platform, the implications it – and other platforms like it - have for businesses both commercially and strategically are vast. At Leighton our early experience with AI-assisted spec-driven development suggests material productivity gains – with our AWS Practice estimating up to a 6x increase in output and delivery speed when used effectively. Our time is then spent refining plans, updating steering docs, incrementally reviewing code as tasks are competed, and planning the next iteration of changes.
The system can be used to facilitate faster prototyping and validation with customers by allowing iteration in real time. This in turn shortens feedback loops and provides a quicker path from concept to minimum viable product (MVP). For businesses operating in competitive or time-sensitive markets, this can be a decisive advantage.
By automating manual and repetitive development tasks, Kiro allows organisations to improve resource efficiency – re-allocating skilled engineers to higher-value activities and reducing bottlenecks caused by limited specialist capacity. This has clear implications for cost control, scalability and delivery confidence.
Spec-driven delivery, combined with automated testing and documentation, can also improve consistency across teams and projects, alignment with architectural standards and the quality and maintainability of codebases. Overtime, this can reduce maintenance costs, incident rates and long-term technical debt.
Despite its promise, AWS Kiro is not a plug-and-play productivity tool. Its success depends on deliberate organisational change.
Implemented well, Kiro can shift the emphasis for development teams from manual coding to high-quality specification and design, architecture, integration and security oversight and continuous review and validation of AI-generated outputs. This naturally means senior developers will be essential to successful implementation.
Organisations wishing to use these tools effectively must re-think their entire approach to the software development lifecycle especially team structures, workflows and performance measures. In order to harness the full power of AI and its role in the work we deliver, companies need to avoid simply overlaying AI onto outdated delivery models which can exacerbate existing inefficiencies. In addition, to use Kiro effectively, teams need strong product skills, experience in prompt engineering and AI steering and senior engineers capable of validating and guiding AI output.
This might drive organisations towards smaller teams with greater depth of experience, rather than larger teams focused on manual execution. However, one of the most significant long-term risks is over-reliance on AI tools. The oversight and management by senior developers will remain essential and so junior engineers still need to learn how to design and write software manually to ensure the right talent in the future. Without these foundational skills, they cannot validate AI-generated code and organisations risk future skills shortages if training is neglected. This makes a commitment to structured learning pathways and deliberate skills development fundamental for companies.
Beyond its impact on the workforce, there is also a complex governance, compliance and IP landscape to be navigated. Automated code and documentation raise important questions around intellectual property ownership, regulatory compliance, auditability and accountability and security and data handling.
These considerations must be addressed within legal, risk and governance frameworks – before deployment.
Using AI-powered tools to assist with spec-driven development presents some huge opportunities for our industry. The organisations that succeed in maximising on those opportunities will be those that embrace spec-driven development as a business discipline, not just a technical one, and assess how AI can support that discipline in a structured way. In addition, those that invest in senior capability, governance and foundation training for junior team members to balance automation with human oversight and accountability will likely see more success. The way to maximise the impact systems like Kiro have at an operational level will be by treating AI as a collaborative team member, not a replacement.
For leaders, the question is no longer whether AI will impact software delivery but how quickly and how deliberately they adapt their operating models to capture value while managing risk.
At Leighton, we see AI-assisted spec driven development as catalysts for meaningful change – but only when paired with the right strategy, capability and commercial mindset. If you’re considering embedding Kiro – or a similar tool – across your organisation we’re here to help. Contact our AWS Practice and they’ll be happy to help!
The rapid evolution of AI in software engineering has produced no shortage of coding assistants, copilots and experimentation tools. Many promise productivity gains, but few fundamentally reshape how software is designed, governed and delivered.
Kiro, which was a main feature of AWS re:Invent, represents a more meaningful shift. Rather than accelerating individual coding tasks, Kiro introduces a native spec-driven, agentic approach to software development that has the potential to materially change delivery models, team structures and commercial outcomes across the industry.
For organisations looking beyond experimentation and toward production-grade impact, Kiro raises both significant opportunities and important strategic challenges.
Kiro is an AI-enhanced development platform (IDE and CLI) designed to support end-to-end, spec-driven software delivery. Unlike traditional coding assistants that are not native spec-driven first, or mainly focuson inline suggestions or code completion, Kiro uses intelligent agents to help teams:
In practical terms, developers can describe features or fixes in plain English – for example, structured user stories using formats such as EARS – and Kiro produces complete, testable and production-ready outputs aligned to those specifications.
Kiro also supports native steering docs, letting us define our own coding patterns, standards, approaches, and tooling so the system aligns with our organisational practices. This approach shifts AI-assisted development toward repeatable, auditable and business-aligned delivery, positioning AI as a team member as opposed to simply a tool.
Several Kiro capabilities are particularly significant from an enterprise and commercial perspective.
The focus on spec-driven development at scale enforces a discipline where specifications and plans are created before code. This improves alignment between business intent and technical output, reducing re-work and misinterpretation.
In addition, through the creation of steering documents, which outline how we build things and what frameworks and patterns we use, we can guide the model in building things in a consistent way.
Kiro generates integrated code, tests and documentation including application code, automated tests and supporting documentation (e.g. Markdown, Mermaid diagrams, API definitions). This also offers an additional level of consistency across the development cycle and improves maintainability, while also supporting governance, onboarding and compliance requirements.
Agent hooks and workflow automation allows teams to automate repetitive tasks, for example, automatically updating OpenAPI specs when endpoints change, enforcing architectural or coding standards and keeping documentation in sync with implementation, helping organisations maintain quality at speed as systems evolve.
Finally, perhaps one of the most interesting features of Kiro is the ability for teams to interact with the platform as a collaborative “team member” allowing them to ask questions, explore design options, debug issues, refactor components and integrate in real-time. This new approach paves the way for true integration into the development cycle, complementing skilled developers by supporting them with repetitive, time-consuming tasks and freeing up resource for more complex parts of the development cycle.
While Kiro is a technical platform, the implications it – and other platforms like it - have for businesses both commercially and strategically are vast. At Leighton our early experience with AI-assisted spec-driven development suggests material productivity gains – with our AWS Practice estimating up to a 6x increase in output and delivery speed when used effectively. Our time is then spent refining plans, updating steering docs, incrementally reviewing code as tasks are competed, and planning the next iteration of changes.
The system can be used to facilitate faster prototyping and validation with customers by allowing iteration in real time. This in turn shortens feedback loops and provides a quicker path from concept to minimum viable product (MVP). For businesses operating in competitive or time-sensitive markets, this can be a decisive advantage.
By automating manual and repetitive development tasks, Kiro allows organisations to improve resource efficiency – re-allocating skilled engineers to higher-value activities and reducing bottlenecks caused by limited specialist capacity. This has clear implications for cost control, scalability and delivery confidence.
Spec-driven delivery, combined with automated testing and documentation, can also improve consistency across teams and projects, alignment with architectural standards and the quality and maintainability of codebases. Overtime, this can reduce maintenance costs, incident rates and long-term technical debt.
Despite its promise, AWS Kiro is not a plug-and-play productivity tool. Its success depends on deliberate organisational change.
Implemented well, Kiro can shift the emphasis for development teams from manual coding to high-quality specification and design, architecture, integration and security oversight and continuous review and validation of AI-generated outputs. This naturally means senior developers will be essential to successful implementation.
Organisations wishing to use these tools effectively must re-think their entire approach to the software development lifecycle especially team structures, workflows and performance measures. In order to harness the full power of AI and its role in the work we deliver, companies need to avoid simply overlaying AI onto outdated delivery models which can exacerbate existing inefficiencies. In addition, to use Kiro effectively, teams need strong product skills, experience in prompt engineering and AI steering and senior engineers capable of validating and guiding AI output.
This might drive organisations towards smaller teams with greater depth of experience, rather than larger teams focused on manual execution. However, one of the most significant long-term risks is over-reliance on AI tools. The oversight and management by senior developers will remain essential and so junior engineers still need to learn how to design and write software manually to ensure the right talent in the future. Without these foundational skills, they cannot validate AI-generated code and organisations risk future skills shortages if training is neglected. This makes a commitment to structured learning pathways and deliberate skills development fundamental for companies.
Beyond its impact on the workforce, there is also a complex governance, compliance and IP landscape to be navigated. Automated code and documentation raise important questions around intellectual property ownership, regulatory compliance, auditability and accountability and security and data handling.
These considerations must be addressed within legal, risk and governance frameworks – before deployment.
Using AI-powered tools to assist with spec-driven development presents some huge opportunities for our industry. The organisations that succeed in maximising on those opportunities will be those that embrace spec-driven development as a business discipline, not just a technical one, and assess how AI can support that discipline in a structured way. In addition, those that invest in senior capability, governance and foundation training for junior team members to balance automation with human oversight and accountability will likely see more success. The way to maximise the impact systems like Kiro have at an operational level will be by treating AI as a collaborative team member, not a replacement.
For leaders, the question is no longer whether AI will impact software delivery but how quickly and how deliberately they adapt their operating models to capture value while managing risk.
At Leighton, we see AI-assisted spec driven development as catalysts for meaningful change – but only when paired with the right strategy, capability and commercial mindset. If you’re considering embedding Kiro – or a similar tool – across your organisation we’re here to help. Contact our AWS Practice and they’ll be happy to help!
