Agentic Development
Accelerate and scale software engineering with governed and secure agentic development that helps teams build, test, and modernize software faster.
What is agentic development?
Agentic development is a software development approach where specific tasks are delegated to one or more orchestrated agents. These agents interpret development goals, as well as plan, design, build, test, and deploy. Humans provide strategic direction to ensure that development is aligned to business, compliance, & operational requirements.
As an AI-native engineering company, 10Pearls leverages over two decades of software development experience to deploy AI-augmented and agentic development practices. Our regulated industry experience shapes our governance-first approach to agentic software development systems, with governance, security, and policy controls built into the agents’ architecture and orchestration.
Our agentic development capabilities
We engineer the roles, context, controls, and development workflows that enable AI coding agents to operate reliably across the software development lifecycle (SDLC).
Agentic delivery framework design
Accelerate adoption by defining agent roles, handoffs, decision boundaries, and checkpoints around your existing software development lifecycle, teams, and tools.
Codebase context engineering
Improve agent accuracy by grounding decisions in repository structure, architecture records, standards, dependencies, and relevant project knowledge and history.
Architecture rule enforcement
Reduce design drift by translating architecture decisions, coding standards, and project conventions into enforceable instructions, automated checks, and gates.
AI-native development workflows
Increase delivery efficiency by embedding AI coding agents into planning, implementation, pull requests, testing, CI/CD, and day-to-day team collaboration workflows.
Automated validation loops
Improve code quality by enabling agents to compile, test, review, inspect failures, and revise work before it reaches human approval or production environments.
Human oversight & approval design
Minimize and control risk by developing approval points, escalation paths, and intervention rules based on task complexity, agent autonomy, and business impact.
Benefits of agentic software development
Using AI agents for software development can deliver several tangible benefits, including improved delivery capacity, consistency, maintainability, and engineering focus.
Enhanced delivery capacity
Scale capacity via parallel task execution and faster iteration cycles as agentic structures and human oversight mature together.
Architectural consistency
Minimize architectural drift and technical debt with consistent application of design rules and conventions across changes and codebases.
Predictable software quality
Enable consistent and dependable software quality through systematic application of engineering standards, automated reviews, and validation loops.
Lower maintenance burden
A lot of engineering hours are saved through ongoing detection and repair of codebase drift, dependency updates, and API repairs.
Governance & auditability
Enhanced development transparency and visibility through built-in governance controls and auditability; ideal for regulated environments.
Improved engineering focus
Better architectures and products as engineers shift from repetitive implementation to innovation, strategy, and higher-value problem-solving.
Where agentic development delivers value
Agentic software development can expand delivery capacity and improve engineering consistency for projects with clear standards, defined boundaries, accessible context, and verifiable outcomes.
New application development
Build new applications faster using reusable foundations, defined standards, and parallel workflows.
High-volume feature delivery
Move well-defined backlog items through planning, implementation, testing, and review in parallel.
Modernization & large-scale refactoring
Map complex codebases, coordinate changes across modules, and validate refactoring at a massive scale.
Test generation & coverage expansion
Create and maintain tests including integration and regression, across large or fast-changing codebases.
Framework, API & dependency migrations
Systematically update frameworks, APIs, and dependencies while testing changes across the codebase.
Cross-repository engineering changes
Apply shared standards, security fixes, and platform updates consistently across code repositories.
Documentation & knowledge capture
Generate, maintain, and update technical documentation, ARDs, and explanations as systems change.
Defect remediation & root-cause analysis
Trace failures across code, logs, and tests, then propose, execute and validate targeted fixes.
Security remediation at scale
Apply validated security fixes across repositories while checking dependencies, tests, and impact.
Agentic development vs. AI-assisted development
As organizations shift from AI-assisted to agentic development, understanding the main differences between the two is essential for a smooth transition.
| AI-assisted development | Agentic development | |
|---|---|---|
| Execution ownership | Humans own and drive the work through AI pair programming. | Agents interpret goals and act within defined boundaries. |
| Human role | Providing context and direction throughout execution. | Define intent, constraints, business alignment, and resolve exceptions. |
| Architectural control | Remains with human developers for overall project. | With humans for overall architecture. With agents for assigned segments. |
| Throughput | Enhanced, dependent on individual skills and process efficiency. | Significantly enhanced through parallelism. |
| Consistency and maintainability | Depends on team discipline and established development practices. | Can be applied with guardrails, structured workflows, and oversight. |
| Security and governance | Existing practices with AI-specific access control and permissions. | Controls built into agent architecture and orchestration. |
| Adoption maturity | Widely adopted across organizations though still evolving. | Emerging. Early adoption in organizations with high AI maturity. |
| Best suited for | Exploratory, investigative, and tasks that require frequent judgment. | Tasks with clear requirements, boundaries, and expected outcomes. |
How we leverage agentic AI across the SDLC
Architecture
Applies project-specific design rules within the module assigned to it, maintains architectural integrity, and maintains ADRs.
Intent refinement
Turns project goals and intent into clear, unambiguous work items and requirements, keeping them aligned to business intent.
Specification development
Converts the work items and requirements into clear technical specification that defines what needs to be built.
Implementation planning
Maps affected modules and dependencies across the codebase, then define a clear implementation plan for the work.
Foundation development
Creates reusable functions, utilities, abstractions, and core components that support consistent feature development.
Feature development
Writes code for the features, building on the approved foundations built in the previous step, while following project conventions.
Quality reviewer
Reviews code, security, and risk through a PR-style gate, flagging issues before changes are approved or merged.
Maintenance & remediation
Fixes dependency issues, deprecated APIs, broken builds, and regressions to keep the codebase stable and current.
Why choose 10Pearls for agentic development
Development experience
We bring over 20 years of software delivery experience shaped by 700 projects for 225 clients, including Fortune 1000 enterprises.
Agentic capabilities
From agent architecture to drift monitoring, our end-to-end agent engineering depth makes our agentic development systems resilient in production.
Governance & security
As an ISO 27001 certified company with deep regulated industry experience, we build governance and security controls into agentic architectures.
Architectural capabilities
We assess your existing architecture and data infrastructure, then design agentic development systems to integrate cleanly.
Use case alignment
Reduce experimentation risk and accelerate software delivery by identifying optimal use cases for which agentic development is a viable fit.
Opportunity identification
Identify new products and workflows where agentic development can create business value beyond improving existing engineering processes.
Keeping humans in control
Agentic development does not remove engineers from the process. Humans retain responsibility for consequential decisions, while agents operate within defined boundaries and engineering controls are applied throughout execution.
Code quality
Concern
The code looks right, but it’s wrong in a subtle way.
Response
Every change goes through the Quality Reviewer gate and automated testing. Some changes, including high-risk and critical ones require human approvals before they are approved.
Human accountability
Concern
Who is accountable for decisions made by agents?
Response
Accountability remains with the human engineers who define intent, approve critical architecture decisions, set boundaries for agents, and act as the final approval authority.
Architectural integrity
Concern
Autonomous development may increase technical debt.
Response
Humans are responsible for setting the architecture and engineering standards. The Architecture Guardian applies those rules and flags drift before it becomes harder to fix.
Security & compliance
Concern
Agents with sufficient autonomy may lead to higher security and compliance risk.
Response
The controls for security, access, and policy are built into the agent architecture (individual) and orchestration. Each change is checked against the requirements and risk levels of the environment.
Full traceability
Concern
Engineers may not understand
agent-generated code.
Response
Plans, code changes, test results, and review notes are recorded all through the agentic development workflows. So engineers have clarity on each change and its history.
Risk-based approvals
Concern
Human approvals may erode the
speed advantage.
Response
Approvals and oversight are matched against risk. Humans get to approve most critical and consequential decisions, while low-risk decisions pass through automated checks.
FAQs about agentic development
What is agentic coding?
Agentic AI coding is a development approach in which AI agents take on defined software tasks, decide the required steps, use engineering tools, write and test code, and revise their work with minimal human direction.
Does agentic development require coding knowledge?
Yes. While agentic development changes the SDLC significantly, it doesn’t remove the need for technical judgment that requires coding knowledge and experience. Developers are responsible for defining requirements, reviewing decisions, managing overall architecture, and resolving exceptions before the code is ready to move ahead.
How do AI coding agents write code autonomously?
AI coding agents work from a goal, gather context from the codebase, plan the task, update files, run tests or checks, and respond to the results. Their autonomy depends on the permissions, tools, boundaries, and approval rules built into the development environment.
How is agentic development different from Copilot or AI pair programming?
AI pair programming keeps the developer in control of each interaction. Agentic development goes further by allowing agents to carry a bounded task through multiple steps, coordinate with other agents, use tools, test their work, and return a reviewed result.
Does agentic development replace developers?
No. Agentic development simply shifts the responsibility of developers to a more strategic role. They make critical product decisions, design overall architecture, review high-risk work, and handle exceptions, instead of writing and validating code.
How do you keep AI-generated code secure and maintainable?
The security and maintainability of agent-generated code come from the system around the agents. This includes restricting access to allowed tools, enforcing coding and architecture standards, and automated testing. Other important controls are built into the agentic development systems, including security checks, approval gates, auditability, and human review of high-risk or critical changes.
Accelerate delivery with agentic development
10Pearls enables governed, auditable, human-in-the-loop agentic software development that expands delivery capacity without compromising quality, security, or control.