Task Agents
01 / 05Single-purpose agents that own one function end to end — triaging support tickets, qualifying leads, or extracting data from documents — with a narrow, well-defined tool set.
scoping · task ownershipsprint 01
AI Agent Development Company for Custom, Production-Ready AI Agents
We design, build, and deploy custom AI agents — from single-task assistants to multi-agent systems — wired into your real tools, data, and workflows.
40+
agents shipped
to production
to production
4
domains: support, sales,
finance, ops
finance, ops
30
day hypercare on
every rollout
every rollout
01 · Definition
What Is AI Agent Development
Perceive · reason · act · evaluate
// the practice
AI agent development is the practice of designing and building software agents that can perceive input, reason about a goal, take action through tools or APIs, and evaluate the result — with a defined degree of autonomy rather than a fixed decision tree.
// what makes it distinct
Agentic AI development differs from conventional software development in one key respect: instead of coding every branch of logic explicitly, we define the agent's goal, its available tools, and its guardrails, and the agent determines its own path to the outcome. This is what makes agentic AI development a distinct discipline from typical backend engineering — it requires prompt architecture, tool design, and evaluation methodology alongside standard software skills.
02 · Methodology
How to Develop an AI Agent: Our Approach
Teams asking how to develop an AI agent often start by choosing a framework before deciding what the agent should actually do. Building agents that hold up in production begins with three decisions, made before any code is written: which specific decision or action the agent will fully own, which tools it is allowed to call, and at what confidence level it hands off to a human.
// then the build
The same sequence, every project
Once those are settled, the technical build becomes straightforward: choose a base model, define how the agent stores information and tracks context, build a controlled interface for the tools it can call, and construct an evaluation set before the agent touches live data. We follow this same sequence on every project, whether we're building a single-task agent or a full multi-agent system.
03 · Agent types
Types of AI Agents We Develop
Coralsoft delivers AI agent development solutions across the agent types businesses ask for most.
Multi-Agent Systems
02 / 05Coordinated agent teams where a planner agent decomposes a task, specialised sub-agents execute steps, and a supervisor agent reviews outputs before they reach a system of record.
Retrieval-Augmented Agents
03 / 05Agents grounded in your own data through RAG pipelines, so responses and decisions are based on your documents, policies, and records rather than the model's general training data.
Tool-Using and API Agents
04 / 05Agents that call external APIs, internal services, or business systems directly — booking, provisioning, updating records — under scoped, auditable permissions.
Voice and Conversational Agents
05 / 05Agents built for real-time conversation, handling multi-turn dialogue, interruption handling, and context retention across a live interaction.
04 · Architecture
AI Agent Development Architecture
Custom AI agent development is an architecture discipline as much as a prompting one. The decisions below determine how the agent behaves once it meets real, unpredictable input.
01 / 03
Reasoning and Planning Layer
We define how the agent breaks down a goal into steps — single-pass reasoning for simple tasks, iterative planning loops for tasks with dependencies or open-ended scope.
02 / 03
Tool and Memory Layer
Each agent is given a defined tool set and a memory strategy — short-term context for a single session, long-term memory where the agent needs to recall prior interactions.
03 / 03
Guardrails and Evaluation Layer
Confidence thresholds, action limits, and automated evaluation sets are built in from the first sprint, not added after an incident.
Technology Stack Reference
// table 01 — stackReference AI Agent Stack
/ 5 layers| Layer | Components | Technology Examples |
|---|---|---|
| Reasoning | Core model powering agent decisions | OpenAI, Anthropic, open-source models |
| Agent Layer | Planning, delegation, orchestration | LangGraph, CrewAI, custom orchestration |
| Retrieval | Grounding in proprietary data | Pinecone, pgvector |
| State & Memory | Session and long-term memory | Postgres, Redis |
| Observability | Monitoring, evaluation, cost tracking | Datadog, Langfuse |
05 · Build choice
Custom AI Agent Development vs Off-the-Shelf Tools
No-code agent builders are a reasonable starting point for a narrow, low-stakes task. Custom AI agent development becomes the right call once a workflow involves proprietary data, multiple internal systems, non-standard decision logic, or volume that makes per-seat SaaS pricing uneconomical. Most teams that start with a builder and hit one of these limits end up rebuilding on custom infrastructure within a year.
// four limits
06 · Process
Our AI Agent Development Process
We follow a five-stage process refined across dozens of agent development engagements.
// 5 stages
Scoped before built, hardened before live.
- 01
DiscoveryDiscovery and Task Scoping
We define exactly what decision or action the agent will own, the tools it needs, and the guardrails required before writing a line of code.
- 02
DesignAgent Design
We design the reasoning approach, tool interfaces, and memory strategy, and document the trade-offs for each choice.
- 03
DevelopmentBuild and Integration
Agent logic and system integrations delivered in two-week sprints with working demos against real data and tasks.
- 04
HardeningEvaluation and Hardening
Testing against historical cases, adversarial inputs, and load conditions to confirm the agent behaves predictably before go-live.
- 05
DeploymentDeployment and Continuous Improvement
Phased rollout, a 30-day hypercare period, and ongoing tuning as the agent encounters new input patterns in production.
07 · Deployments
Where We've Deployed AI Agents
Customer Support
01 / 06Ticket triage, response drafting, and escalation agents integrated with existing helpdesk platforms.
Sales & Revenue Teams
02 / 06Lead qualification and CRM-updating agents that hand off warm leads at the right moment.
Finance & Operations
03 / 06Invoice processing, reconciliation, and compliance-check agents with human sign-off on flagged items.
Healthcare Administration
04 / 06Intake and scheduling agents grounded in policy documents via retrieval, with mandatory human review.
E-commerce
05 / 06Order-status, returns, and product-question agents integrated with storefront and fulfilment systems.
Internal IT & HR
06 / 06Onboarding, access-request, and internal support agents wired into existing ticketing systems.
08 · Why Coralsoft
Why Choose Coralsoft as Your AI Agent Development Company
Coralsoft works as a dedicated AI agent development company — our team is accountable for the agent's real-world performance, not just its demo.
Agentic Software, Not Chatbot Wrappers
We build genuine agentic AI — agents that plan, use tools, and adapt — rather than a scripted chatbot with an LLM front end.
Deep System Integration
Our agents read from and write to the systems your team already uses, scoped to exactly the access each task requires.
Production-Grade Reliability
Every agent ships with monitoring, cost tracking, and defined guardrails — agentic systems that fail safely and visibly, not silently.
Cross-Domain Agent Experience
Our team has delivered AI agents across support, sales, finance, and operations workflows, carrying pattern-level lessons from one engagement into the next.
09 · Selected work
Case Studies
Coralsoft has developed and deployed AI agents in production environments where accuracy, auditability, and reliability all had to hold under real load.
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 study10 · Engagement
Engagement Models
We structure engagements to match your stage — from a single pilot agent to an ongoing agentic development partnership across multiple teams.
Single-Agent Pilot
01One task, fully scoped and hardened. Typically $10,000–$30,000. Best for validating agentic automation before wider investment.
// one task$10K–$30K
Multi-Agent System
02Several coordinated agents with integrations, memory, and evaluation pipelines. Typically $40,000–$150,000+, depending on scope and system count.
// coordinated agents$40K–$150K+
Ongoing Partnership
03Continuous agent development and tuning across teams as new use cases and input patterns emerge.
// continuousRetainer
11 · FAQs
FAQs
The questions teams ask most before commissioning an agent. Anything else, ask us directly.
12 · Ready when you are
Your Next Employee Doesn’t Sleep
Tell us what task you want an agent to own. We will map the architecture, tools, and guardrails, and give you a realistic cost estimate — in one 45-minute call. No obligation.
- 45-minute discovery call
- Architecture, tools & guardrails map
- Realistic cost estimate
- No obligation