scoping · the task firstsprint 01

AI Agent Development Frameworks Services for Production-Ready Agents

We design and build custom AI agents using the frameworks and tools best suited to your task — not a single default stack applied to every project.

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frameworks in
active use

evaluation criteria
per project

day hypercare on
every rollout

01 · Definition

What Are AI Agent Development Frameworks

Framework-agnostic by design

// where they sit

AI agent development frameworks are the software libraries and orchestration layers that structure how an AI agent plans, calls tools, manages memory, and coordinates with other agents. They sit between the base model and your application, handling state, tool routing, and multi-agent communication so that logic doesn't have to be hand-built from scratch for every agent.

// why it matters

Working with the right framework is what separates a production-grade agent from a prompt-and-a-loop prototype: proper framework selection gives you debuggability, controlled tool access, and a path to scale from one agent to a coordinated multi-agent system.

02 · Frameworks

Popular AI Agent Development Frameworks We Build With

Coralsoft is framework-agnostic — we work across the popular frameworks in production use today, and select the one that fits your task rather than defaulting to a single tool.

LangGraph

01 / 06

Used for agents with real branching logic and complex state — graph-based orchestration gives explicit, debuggable control over execution order.

CrewAI

02 / 06

Used when a project calls for a role-based team of agents delegating tasks to one another, with fast time-to-first-working-version.

AutoGen

03 / 06

Used for conversational multi-agent setups and Microsoft-ecosystem projects, where agents solve a task by exchanging messages.

OpenAI Agents SDK

04 / 06

Used for OpenAI-native builds requiring tight integration with tool calling, handoffs, and guardrails at the model-provider level.

LlamaIndex Workflows

05 / 06

Used for retrieval-heavy, document-grounded agents that need deep integration with a knowledge base.

Semantic Kernel

06 / 06

Used for agents embedded into existing .NET or enterprise-Java applications.

03 · Selection

How We Select the Best Framework for Your Project

We don't start with a framework — we start with the task, then work backward to the tooling. Our internal comparison runs against five criteria specific to your project: orchestration complexity, multi-agent requirements, existing infrastructure, team familiarity, and long-term maintainability.

// documented, not assumed

Decided during discovery, in writing

This evaluation happens during discovery, before implementation begins, and the outcome is documented — so you understand why a given framework was chosen, not just which one was chosen. For most projects, the right decision is not a single tool but a primary framework plus one or two supporting libraries for retrieval, memory, or evaluation.

04 · Reference

Framework Selection Reference

A quick view of how framework choice tends to map to project shape — used as a starting hypothesis during discovery, not a final answer.

// table 01 — fit

Project Shape → Framework Fit

/ 6 shapes

Project Shape	Typical Framework Fit	Why
Complex branching logic, dependencies	LangGraph	Explicit, debuggable graph-based control flow
Role-based agent team	CrewAI	Fast to stand up, natural task delegation model
Conversational multi-agent, MS stack	AutoGen	Message-passing agents, Microsoft ecosystem fit
OpenAI-native build	OpenAI Agents SDK	Tight tool calling and handoff integration
Document-grounded retrieval agent	LlamaIndex Workflows	Deep retrieval and indexing integration
.NET / enterprise Java embedding	Semantic Kernel	Native fit with existing enterprise stack

05 · Architecture

AI Agent Development Frameworks Architecture

Regardless of which framework anchors the build, every engagement follows the same architectural discipline.

01 / 03

Reasoning and Orchestration Layer

The chosen framework structures how the agent plans and executes steps — single-pass for simple tasks, iterative or graph-based for tasks with dependencies.

02 / 03

Tool and Memory Integration

Tools are exposed to the agent through a scoped, auditable interface, with a memory strategy — session-only or persistent — matched to the task.

03 / 03

Evaluation and Observability

Every framework we deploy is wired into an evaluation harness and observability stack (Langfuse, Datadog, or custom dashboards) from the first sprint, not added after launch.

06 · Process

Our AI Agent Development Process

A five-stage process, framework-agnostic at every step until the discovery stage determines the right tooling.

// 5 stages

Framework-agnostic until discovery says otherwise.

01
DiscoveryDiscovery and Framework Evaluation
We scope the task and run our framework comparison against your specific requirements before selecting the stack.
02
DesignAgent and Tool Design
We design the agent's reasoning approach, tool interfaces, and memory strategy within the chosen framework.
03
DevelopmentBuild and Integration
Agent logic and system integrations delivered in two-week sprints with working demos against real data.
04
HardeningEvaluation and Hardening
Testing against historical cases, adversarial inputs, and load conditions before go-live.
05
DeploymentDeployment and Continuous Optimisation
Phased rollout, a 30-day hypercare period, and ongoing tuning as frameworks and models evolve.

07 · Applications

Where Framework Choice Matters Most

Enterprise IT

01 / 06

Existing .NET or Java investment favours Semantic Kernel over a ground-up rebuild.

Fast-Moving Startups

02 / 06

CrewAI's speed-to-first-version suits teams validating an agent concept before scaling it.

Regulated Industries

03 / 06

LangGraph's explicit control flow suits processes that need auditable, step-by-step decision trails.

Knowledge-Heavy Products

04 / 06

LlamaIndex Workflows suits agents that must stay grounded in a large, evolving document base.

Microsoft-Ecosystem Teams

05 / 06

AutoGen fits naturally where the surrounding stack is already Microsoft-centric.

OpenAI-Native Products

06 / 06

The OpenAI Agents SDK suits teams standardised on OpenAI's tool-calling and handoff model.

08 · Why Coralsoft

Why Choose Coralsoft for AI Agent Development Frameworks Services

Most agencies default to one framework because it's the one their team already knows. We maintain working expertise across the top frameworks specifically so the tool serves the task, not the other way around.

Framework-Agnostic by Design

Our recommendation is based on your task and infrastructure, not a single framework we're incentivised to reuse across every client.

Documented Decision-Making

Every framework choice comes with a written rationale — the trade-offs considered and why the selected stack won out for your specific case.

Production Discipline Across Any Framework

Evaluation, observability, and guardrails are applied consistently regardless of which framework anchors the build.

09 · Engagement

Engagement Models

We structure engagements to match your starting point — from a framework evaluation and pilot to a full multi-agent build on the stack we jointly select.

Framework Evaluation & Pilot

A scoped comparison against your task, followed by a single-agent pilot on the recommended stack. Typically $10,000–$30,000.

// evaluate then pilot$10K–$30K

Multi-Agent Build

A full system with integrations, memory, and evaluation pipelines. Typically $40,000–$150,000+, depending on scope and system count.

// full system$40K–$150K+

Time & Materials

Flexible engagement for evolving agent scope or migrating between frameworks as requirements change.

// flexibleHourly

10 · FAQs

FAQs

The questions teams ask most when choosing an agent framework. Anything else, ask us directly.

What are the best AI agent development frameworks today?

LangGraph, CrewAI, AutoGen, the OpenAI Agents SDK, LlamaIndex Workflows, and Semantic Kernel are the frameworks we deploy most often, depending on the project's orchestration complexity, multi-agent needs, and existing infrastructure. We select rather than default to one across every engagement.

What is the best framework for AI agent development?

There isn't a single best framework — the right choice depends on your task's branching complexity, multi-agent requirements, and existing tech stack. We run this evaluation as part of discovery for every project.

Do you compare frameworks before starting a project?

Yes. Every engagement starts with a framework evaluation scoped to your specific task, infrastructure, and team, with the reasoning documented before implementation begins.

Can you build with a framework our team already uses?

Yes — if your team has existing investment in a particular framework or infrastructure, we build on it rather than introducing a new stack, provided it fits the task's requirements.

How much does AI agent development cost, regardless of framework?

A single-agent build typically ranges from $10,000 to $30,000. Multi-agent systems with integrations, memory, and evaluation pipelines range from $40,000 to $150,000+, depending on scope and the number of systems involved — largely independent of which framework is used.

11 · Ready when you are

The Right Framework. The Right Agent. Built Once.

Tell us what you need the agent to do. We will run the framework comparison, map the architecture, and give you a realistic cost estimate — in one 45-minute call. No obligation.

45-minute discovery call
Framework comparison & architecture map
Realistic cost estimate
No obligation