Using LLM Agents for Software Development

AI Agents, GenAI, Startups | August 23, 2025 | Albert Anthony

“If you’ve been in a software development business for a while, you’ll understand how AI, agents, and LLMs change the game. Earlier, you were limited by an algorithm you’d put in place for your software. Now that algorithm can be replaced or supported by an ever-evolving LLM, making your software improve every single time an LLM updates. Every single software out there is your TAM for this use case i.e. Using LLM Agents for Software Development.

Those who understand this paradigm shift are going to make a killing, either as an individual contributor or as an entrepreneur.”

-Albert

Founder – Loves Cloud

The shift: deterministic code to LLM agents for software development

Traditional software ships deterministic logic. You encode an algorithm, test it, and release it. Updates require a new build. LLM-enabled systems introduce an adaptive layer. A single prompt, policy, or retrieval source can change behaviour without rewriting code. Model upgrades can improve reasoning or tool use. This changes how teams design, ship, and operate products.

Key implications:

Behaviour becomes data and policy driven, not only code driven.
Product surfaces can adapt across intents and edge cases that were not hard coded.
Evaluation, monitoring, and guardrails become part of the release process.
FinOps extends to tokens, context windows, and tool calls, not only compute and storage.

What this looks like in daily work

Search and support: Answers move from keyword match to retrieval-augmented reasoning over your docs, tickets, and runbooks.
Workflows: Agents orchestrate steps such as data extraction, validation, enrichment, and handoff to systems.
Personalization: Prompts and policies tailor tone and depth per user role.
Decision support: LLMs summarize trade-offs and highlight risks, while humans approve.

Architecture patterns that show up repeatedly

RAG: Bring your own trusted knowledge to the model at query time. Keep sources versioned.
Tools and actions: Let the model call APIs for read and write operations. Define strict schemas.
Guardrails: Constrain inputs and outputs, enforce policies, and route unsafe or ambiguous cases to humans.
Evaluation harness: Test prompts and policies with golden datasets, unit tests for tool use, and regression checks.
Observability: Track latency, cost, success rate, and failure reasons at the task level.

Product lifecycle changes – LLM agents for software development

Releases become continuous: Prompt, retrieval, and policy changes ship faster than code releases.
Model upgrades are product events: New models may improve accuracy or create regressions. Treat upgrades like feature launches with staged rollouts and evals.
Experimentation first: A/B prompts, policies, and retrieval strategies. Measure business impact, not only token metrics.

About the “everything is TAM” idea

The opportunity is broad because many software surfaces include language, reasoning, or orchestration. That said, suitability depends on:

Availability of high-quality, domain-specific data.
Tolerance for probabilistic outputs and human-in-the-loop processes.
Compliance and security constraints.
Real unit economics after you factor in tokens, caching, and tool calls.

Treat the TAM expansion as a hypothesis. Validate it category by category with pilots and clear metrics.

Risks to manage early

Cost drift: Token and tool usage can spike without limits.
Quality variance: Model changes or retrieval errors can degrade outcomes.
Data exposure: Prompts, logs, and tools may leak sensitive information if not controlled.
Compliance: Some outputs need audit trails and explainability.

A practical 6-month roadmap

Days 0 to 30: Discovery and proofs

Map top workflows where language and reasoning dominate: support triage, ops runbooks, sales research, policy summarization.
Collect representative inputs and target outputs. Define acceptance criteria.
Build two small proofs: one retrieval task, one agent task with a single tool.
Instrument metrics: success rate, time saved, cost per task.

Days 31 to 90: First production slice

Add guardrails, human-in-the-loop, and safe failure modes.
Create an evaluation harness with golden datasets and unit tests for tools.
Introduce caching strategies and budget caps.
Ship to one team with service level objectives and a rollback plan.

Days 91 to 180: Scale and standardize

Expand tool use to key systems. Add tracing and analytics at the workflow level.
Create prompt and policy repositories with versioning and approvals.
Formalize FinOps for AI: budgeting, anomaly alerts, and weekly cost reviews.
Plan model upgrade cadence with pre-flight evals and canary rollouts.

Measures that matter

Success rate: Percent of tasks meeting acceptance criteria.
Cost per task: All-in cost including tokens and tool calls.
Time to completion: Human minutes saved per task.
Escalation rate: Percent routed to human review.
Regression deltas: Quality change after model or prompt updates.
Coverage: Share of workflow volume handled by the agent.

Operating model and roles

Product defines outcomes and guardrails.
Engineering owns tooling, safety, and reliability.
Data curates retrieval sources and evaluation datasets.
Ops reviews exceptions and feeds improvements back into prompts and policies.
Security and compliance establish logging, retention, and audit processes.

For individual contributors and founders

Start with one real workflow and measurable targets.
Treat prompts, policies, and tools as first-class artifacts that deserve reviews.
Document failure modes and who handles them.
Share results, especially cost per task and time saved, to earn adoption.

Checklist for your first agent

Problem statement and acceptance criteria written in plain language
Minimal prompt with system, task, and constraints
One reliable tool with strict schema
Retrieval source with citations and versioning
Offline evaluation set and pass-fail thresholds
Budget limits, caching plan, and alerting
Human review and rollback steps

FAQ style guidance

Will an LLM update automatically improve my product? It can, but not always. Treat upgrades as changes that require evaluation, canaries, and rollback.
Is agentic design overkill for simple tasks? If a task is one step and deterministic, a standard API may be better. Use agents when multi-step reasoning or tool orchestration matters.
How do I control cost? Cap tokens per call, prefer smaller models where quality allows, cache frequent prompts and retrieval results, and measure cost per task weekly.
What about safety? Enforce content and action policies, restrict tools, log traces, and route ambiguous cases to human approval.

A simple action plan you can start this week

Pick one text-heavy workflow.
Write the acceptance criteria and 20 example cases.
Build a minimal RAG or one-tool agent.
Add a reviewer step for the first 100 runs.
Publish results to your team with success rate and cost per task.
Decide to scale, iterate, or halt based on evidence.

How Loves Cloud can help

Loves Cloud supports teams that want to add GenAI and agent capabilities without losing control of cost, security, or governance. Our core competencies include AI and GenAI software development services, FinOps for AI and cloud, and Azure and Microsoft 365 management.

We provide assessments, architecture design for RAG and agent workflows, evaluation harness setup, guardrails and observability, and cost optimization through PowerBoard and OfficeBoard.

If you want a pilot with LLM agents for software development that targets one workflow with measurable success criteria and a clear cost model, we can help you plan and execute it.