Agentic Coding Services for Faster Delivery

Most software projects don't fail because developers write bad code. They fail because the gap between "we have a requirement" and "there's working software in production" is filled with hand-offs, context-switching, and review cycles that compound every day. AI-powered software development changes where that gap lives: not by removing human judgment, but by automating the mechanical work that consumes most of a senior engineer's week.

At Laxaar, we've rebuilt our delivery workflow around agentic coding — a model where autonomous coding agents handle first-pass implementation, refactoring, test generation, and documentation, while engineers focus on architecture decisions, edge cases, and code review. The result is a measurable reduction in cycle time without trading away the quality bar that production systems require.

This isn't about replacing engineers. Agents working without human checkpoints produce plausible code, not correct code. The moat is the review layer and the domain logic we build around it, things no commodity agent loop can replicate.

What you'll learn

What agentic coding actually means in practice
How the Laxaar agentic delivery workflow runs
Where agents save real time — and where they don't
The human review layer that makes it production-safe
Tooling stack we use across projects
Agentic coding vs traditional outsourcing: a direct comparison
How to engage Laxaar's agentic coding services
Frequently Asked Questions

What agentic coding actually means in practice

Agentic coding is software development where an AI agent operates autonomously across multiple files and tasks: reading context, writing code, running tests, observing failures, and iterating. It's the difference between an assistant that finishes your sentence and one that goes off, builds the feature, runs the CI suite, fixes the failing tests, and comes back with a pull request.

The key word is autonomously. The agent is given a task specification, access to the codebase, and a set of tools (shell, file system, test runner, linter), then it drives the implementation loop without hand-holding. This is categorically different from tab-complete tools like GitHub Copilot or Cursor's autocomplete mode, which augment a developer's keystrokes rather than replacing a task.

Practically, this means a well-specified feature card that might take a junior developer two days of implementation time can have a working first draft — with tests, with documentation, with type annotations — ready for senior review within hours. The senior engineer's job shifts from writing to directing and reviewing.

How the Laxaar agentic delivery workflow runs

Our workflow has four repeating stages, each with a clear handoff point.

1. Spec writing. Every task starts with a written specification that defines inputs, outputs, error cases, and acceptance criteria. We write these before the agent touches any code. A vague spec produces a plausible but wrong implementation, so the spec is the executable contract.

2. Agent implementation pass. The coding agent reads the spec, explores the relevant parts of the codebase, writes the implementation, generates tests, and iterates until the test suite passes. We give it a maximum step budget to prevent runaway loops.

3. Engineer review. A senior engineer reviews the diff — not the full file, the diff. They're looking for security issues, architectural drift, misunderstood edge cases, and anything the agent confidently got wrong. This step typically takes 20-40% of the time it would've taken to write the code from scratch.

4. Integration and observability. Merged code goes through the same CI/CD pipeline as hand-written code. We don't have a "soft" path for agent-generated work. If it doesn't pass the same gates, it doesn't ship.

The loop is tighter than a traditional sprint cycle because the agent iteration step is minutes, not days. Engineers spend their time on the parts that actually require human judgment.

Where agents save real time — and where they don't

Agents don't save time uniformly. Knowing which tasks to route to an agent and which to keep with an engineer is itself a skill.

Tasks where agents consistently pull ahead:

Boilerplate scaffolding (new API endpoints, data models, CRUD layers)
Test generation from existing implementations
Refactoring with a well-defined target shape (e.g., migrate from one library version to another)
Documentation and inline comment generation
Type annotation passes on untyped codebases
Repetitive cross-cutting changes (rename a field across 40 files)

Tasks that stay with engineers:

Initial system architecture and service boundary decisions
Security-sensitive design (auth flows, permission models, cryptographic choices)
Novel algorithms where correctness requires mathematical reasoning
Debugging production incidents with unclear root cause
Performance optimization requiring profiler interpretation

The honest trade-off: agents are fast but confident in the wrong direction. They'll produce code that compiles, passes the happy-path tests, and looks reasonable in review, while quietly mishandling an edge case that only appears under load. Senior review isn't optional. It's what converts agent output from "probably fine" to "we'd stake our name on it."

The human review layer that makes it production-safe

Reviewing agent-generated code requires a different mental model than reviewing code a person wrote. Human reviewers instinctively check for intent drift ("did the developer misunderstand the requirement?") and implementation style. Agent review adds a third check: confident hallucination ("did the agent invent a method that doesn't exist, or handle an error path by silently swallowing it?").

Our review checklist for agent PRs adds three items to our standard review guide:

External call verification. Every API call, database query, and file system operation gets checked against the actual SDK docs, not the agent's recollection of them.
Error handling audit. Agents tend to stub error paths with pass, console.error, or optimistic returns. We treat any bare exception handler as a required fix, not a style note.
Test quality check. Agents write tests that test their own implementation. A test that mocks the function it's testing isn't a test. We look for this specifically.

This review pattern adds roughly 30 minutes per feature card. That's the cost, and it's worth stating plainly. The net is still faster than traditional development, but it's not zero overhead.

Tooling stack we use across projects

The specific tools matter less than the workflow discipline, but here's what we reach for at Laxaar across most projects:

Coding agents:    Claude Code (complex multi-file tasks), Cursor (inline editing)
Orchestration:    Custom harness built on top of the Anthropic API (multi-step tasks)
CI/verification:  GitHub Actions (test + lint gate on every agent PR)
Observability:    Langfuse (traces for agent reasoning steps in AI-heavy features)
Spec format:      Markdown task cards with a structured schema (inputs, outputs, ACs)

We don't use a single off-the-shelf agent platform for everything. Platform lock-in is a real cost when the tools are evolving this fast. Our harness is thin on purpose. It manages the step budget, tool grants, and spec injection, then delegates the actual LLM calls to whichever model is best for the task type at the time.

Agentic coding vs traditional outsourcing: a direct comparison

Dimension	Traditional dev shop	Laxaar agentic model
Time to first working draft	2-5 days per feature	2-8 hours per feature
Who writes boilerplate	Junior developers	Agent
Who reviews code	Mid-level peer	Senior engineer
Test coverage	Variable, often low	High by default (agent writes tests)
Documentation	Usually deferred	Generated in the same pass
Iteration speed	1-2 cycles per sprint	Multiple cycles per day
Senior engineer leverage	Mostly writing	Directing and reviewing

The table captures the shape of the difference, not a precise SLA. Actual timelines depend on task complexity, codebase familiarity, and spec quality. A poorly specified card handed to an agent produces rework just as it would handed to a junior developer.

How to engage Laxaar's agentic coding services

We work with three engagement shapes: project-based delivery (fixed scope, defined deliverables), sprint-embedded teams (we join your sprint cycle and own a slice of the backlog), and agentic augmentation (we add our tooling and workflow to your existing team without replacing headcount).

The right fit depends on your situation. If you're a startup that needs an MVP built fast and you don't have a technical team yet, project-based delivery is typically the fastest path. If you have an engineering team that's good but overloaded, augmentation or sprint-embedded usually works better. Your team keeps the architectural knowledge; we accelerate execution.

You can see examples of what we've shipped across industries on our portfolio, or read detailed case studies on our case studies page. If you're trying to figure out the right scope for your project, our custom software development page covers the engagement options in more detail.

To start a conversation, the contact page is the fastest path. We usually respond within one business day with an initial assessment of whether agentic delivery fits your project constraints.

Frequently Asked Questions

What kinds of projects benefit most from agentic coding?

Projects with well-defined requirements, existing codebases with good test coverage, and tasks that involve significant boilerplate or repetitive implementation work benefit most. Greenfield API backends, mobile app feature additions, and data pipeline extensions are strong fits. Projects where the primary challenge is novel algorithm design or deep systems debugging benefit less.

How do you ensure agents don't introduce security vulnerabilities?

Every agent-generated PR goes through the same security review gates as human-written code. We supplement that with an explicit checklist item for external calls and error handling, which are the two areas where agent output is most likely to quietly fail in a security-relevant way. We don't rely on the agent's self-assessment of its own security properties.

Does agentic coding work on existing codebases, or only greenfield projects?

It works on both, though the setup cost differs. An existing codebase with good type coverage and tests gives the agent better grounding — it can read the patterns and follow them. A legacy codebase with minimal tests and inconsistent patterns requires more spec work upfront and tighter review. We've done both and adjusted the workflow accordingly.

How does pricing compare to a traditional development shop?

We don't publish flat hourly rates because the variance is significant by task type. A feature that takes 40 hours of traditional development might take 8 hours with our agentic workflow, so pricing on hours would misrepresent the value exchange. We price on deliverable scope. Visit our quote page for a project-specific estimate.

Can we see the agent's reasoning traces and not just the output?

Yes. For AI-heavy features we capture full reasoning traces through Langfuse and make them available as part of the delivery artifact. For non-AI features (standard web or mobile development using agentic coding internally), we deliver the code and the test suite. The trace is an internal quality tool, not a client-facing deliverable unless you ask for it.

If your team is spending more engineering hours on mechanical implementation than on the architectural and product decisions that actually differentiate your software, agentic coding is worth a direct look. The Laxaar team has shipped production systems across web, mobile, cloud, and AI using this workflow. Reach out and we'll tell you honestly whether it fits your project.

Agentic Coding Services for Faster Delivery at Laxaar