AI Agents
01Use language models, business rules, data, and connected tools to complete defined tasks.
scoping · requirementssprint 01
AI Agent Development Services for Autonomous Operations
Coralsoft designs AI agents that read business context, take approved actions, and send unclear cases to people. From one workflow to a multi-agent system, we build around your data and existing tools.
12
years of delivery experience
200+
integrations delivered
40%
average support load reduction
01 · Overview
What is AI agent development
AI agent development is the process of building software that can assess a task, retrieve relevant information, use approved tools, and complete defined actions. An AI agent can work across systems instead of only answering questions in a chat window. Most businesses consider three related options when planning AI automation.
Agentic AI
02Describes systems that can plan and execute a sequence of steps toward a goal.
AI Agent Development
03Combine model selection, workflow design, data access, security controls, and integration work.
02 · Services
Custom AI agent development services we provide
Every AI agent we build serves a defined business purpose. We focus on the data, actions, and review points that make the workflow useful in daily operations.
Custom AI Agents
01Autonomous agents around your business rules, data, and tools. They assess inputs, select approved actions, and complete them.
Multi-Agent Systems
02Coordinated agents that divide complex work into focused roles, such as research, validation, execution, and review.
AI Copilots
03Internal assistants helping employees find information, prepare drafts, summarise records, and complete routine tasks.
Conversational AI Agents
04Customer-facing agents that answer questions, retrieve approved information, guide users through requests, and complete simple actions.
Workflow Automation Agents
05Agents that execute multi-step processes across connected systems, check conditions, trigger actions, manage hand-offs, and log outcomes.
03 · Industries
Autonomous AI agents for complex operations
The best use cases begin with work that is frequent, rules-aware, and difficult to scale by adding more manual steps. AI agents can support teams in many sectors when the workflow has clear data sources and a defined handoff to people.
Healthcare
01- Prepare intake summaries
- Search approved knowledge
- Route non-routine requests
- Support administrative follow-up
Financial Services
02- Organize documents
- Prepare case summaries
- Flag missing information
- Support controlled review queues
E-Commerce
03- Answer product questions
- Enrich catalog content
- Manage order exceptions
- Support post-purchase requests
Logistics and Manufacturing
04- Track shipment or production exceptions
- Prepare status reports
- Search operating procedures
- Open follow-up tasks
04 · Process
Our AI agent development process
Delivery
Our AI agent development process
We use a staged process for AI agent development services, so technical choices stay linked to real business needs. Every stage creates a decision point before the next layer of work begins.
- 01
Discovery and Assessment
We review the workflow, users, systems, decision points, and failure risks. Together, we define a narrow first use case, success measures, and approval rules.
- 02
Agent Architecture and Guardrails
We map tools, data boundaries, memory, handoffs, and escalation paths. The design identifies where a person must review or approve an action.
- 03
Model Selection and Prototyping
We compare suitable models, retrieval methods, and agent frameworks. A small prototype tests accuracy, latency, cost, and usability before full development.
- 04
Smart Agent Development
We build agent logic, connectors, interfaces, and workflow controls. The agent connects with CRM, ERP, ticketing, or collaboration tools your team already uses.
- 05
Testing and Optimisation
We test realistic scenarios, edge cases, permissions, and error handling. Evaluation tracks task success, grounded answers, action quality, and human overrides.
- 06
Deployment and Adoption
We release in stages, start with a defined group of users, and prepare teams for the new workflow. Production setup can include monitoring, access control, and rollback options.
05 · Technology
Integrations and AI technology
AI agent development requires a practical technology stack. We select components based on the workflow, systems, and security requirements.
Models
01OpenAIAnthropicGoogle Geminiopen-source models
Agent Frameworks
02LangGraphLangChainCrewAIAutoGen
Knowledge Retrieval
03PineconeWeaviatevector searchdocument pipelines
Cloud
04AWSMicrosoft AzureGoogle Cloud
Business Systems
05SalesforceHubSpotSAPNetSuiteJiraConfluenceZendesk
Integration Methods
06REST APIsGraphQLwebhooksOAuthdatabasescustom connectors
06 · Benefits
Benefits of AI agent development
Cost Reduction
01AI agents streamline repetitive tasks, manual checks, and follow-up work. That reduces the ongoing cost of regular operations, while keeping people in play for those cases that are actually complicated and require the skillset.
Faster Execution
02An agent can collect information, prepare context, and trigger the next approved action as soon as an event occurs. Teams spend less time waiting on inboxes, updates, and handoffs.
24/7 Operation
03Agents can receive requests, review available data, and begin approved workflows outside normal business hours. Human teams can review escalations later, with the relevant context already prepared.
Scalability
04AI agents can handle higher volumes of repeatable work without adding the same manual steps each time. Teams can extend workflows with new systems, rules, and use cases.
Improved Decision-Making
05Agents bring approved data, business rules, and relevant history into one workflow. Your employees get better context before making a decision than having to consult multiple systems or partially correct records.
Reduced Manual Effort
06Agents handle repetitive data checks, updates, routing, and notifications. Instead of copying information across tools, teams spend more time managing exceptions, relationships, and higher-value work.
07 · Why Coralsoft
Why choose Coralsoft for AI agent automation
As an AI development agency, Coralsoft combines software engineering, workflow design, system integration, and practical AI controls. We build custom AI agents around your operating model. We focus on clear use cases, dependable integrations, human review paths, and systems your team can monitor and improve after launch.
- Define AI agent use cases with a clear business value
- Select models, frameworks, and architecture based on real requirements
- Connect agents with CRM, ERP, helpdesk, document, and internal systems
- Access controls for sensitive data: permissions, credentials, logs and approval
- Battle-test workflows against edge cases, failure, and high-impact decisions
- Support deployment, monitoring, documentation, and continual improvements
08 · Selected work
Related case studies
Production-grade products. Real users. Measurable outcomes.
Coralsoft NorthLane FreightAn agentic AI dispatcher for autonomous logistics exception resolution
NorthLane Freight
Logistics & supply chainAn autonomous AI agent — not a chatbot, but an agent that takes real actions in external systems — built for a mid-sized US freight brokerage to resolve dispatch exceptions across the TMS, ELD, email, SMS, and carrier portals.
91%
of exceptions resolved fully autonomously, with no dispatcher involvement
4.6 min
average exception response time (down from 41 min)
3.4×
dispatch team throughput, with no headcount increase
LangGraphClaude 3.5 SonnetGPT-4o miniPostgres + pgvectorRedisTwilio
Read the case studyCoralsoft Meridian Billing PartnersAn agentic AI for automating prior authorization in medical billing
Meridian Billing Partners
Healthcare billingAn autonomous AI agent — acting inside payer portals, generating documents, and tracking statuses, not a chatbot — that runs the full HIPAA-compliant prior authorization cycle for a medical billing company serving 22 outpatient clinics.
84%
of PA requests submitted fully autonomously, with no manual form-filling
6.5 hrs
average time from procedure order to request submission (down from 3–5 days)
41%
reduction in denials caused by form-filling errors
LangGraphClaude 3.5 SonnetPlaywrightFHIR / HL7Rules engineAudit logging
Read the case study09 · FAQ
FAQs
Answers to the questions we hear most before kick-off.
Contact
Ready to build an AI agent for your business?
Tell us about the workflow you want to improve. We will map the right architecture, identify integration requirements, and provide a practical estimate for AI agent development services.
- The right architecture mapped for your workflow
- Integration requirements identified
- A practical estimate for AI agent development