AI Agents That Ship to Production
57% of companies already have AI agents running in production (Azumo, 2025). The other 43% are still in pilot because their agents broke under real-world conditions. We build agents that don't break. LangGraph, CrewAI, AutoGen. 50+ systems shipped.
What We Build
Six types of production AI agents. All built on open frameworks. All connected to your real data and systems.
Single-Agent Automations
Focused agents for one job: extract data from documents, qualify leads, triage support tickets, or monitor system alerts. Single purpose, fully automated, no human in the loop unless you want one.
Multi-Agent Systems
Orchestrated networks of specialized agents that hand off work between each other. One agent researches, another writes, a third validates. We use LangGraph or CrewAI for the orchestration layer and connect your existing APIs as tools.
Document Intelligence Agents
Agents that read contracts, invoices, medical records, or compliance docs. They extract structured data, flag anomalies, and push results to your CRM, database, or Slack channel. FinanceLink Australia cut a 3-hour onboarding process to under 5 minutes with this exact pattern.
Customer-Facing Agents
Tier-1 and tier-2 support that resolves without escalating. Product questions, order lookups, account changes: handled. These agents route to a human when they should and don't when they shouldn't. Most standard automation can't make that call.
Dev & CI/CD Agents
Code review agents that flag security issues before merge. Test generation agents that write unit tests from function signatures. PR summary agents that explain diffs to non-technical reviewers. All wired into your existing GitHub or GitLab workflow.
Enterprise Integration Agents
Agents that connect to your actual stack: Salesforce, HubSpot, NetSuite, ServiceNow, Slack, email, internal databases. Not demo agents using mock data. Real integrations that read, write, and act inside your production systems.
Why Afnexis for AI Agent Development
We Know What Breaks in Production
50+ AI systems shipped. Most agent demos fall apart in production because they can't handle edge cases, tool failures, or context windows that overflow at scale. We've hit every failure mode. Our architecture decisions reflect that.
Framework Selection That Matches Your Problem
LangGraph gives full state control and auditability for regulated industries. CrewAI gets multi-role workflows to production 40% faster. AutoGen fits Microsoft-heavy stacks. We pick based on your constraints, not what's trending this week.
Observability From Day One
Every agent we ship includes LangSmith tracing or custom logging. You see exactly what the agent did, what tools it called, what it decided, and why. Not a black box. You can audit any run at any time.
You Own the Code
All agents run on open frameworks you can host yourself. LangGraph, CrewAI, AutoGen: none of these charge per API call to keep your agent alive. No SaaS dependency. You have the code, the infra, and full control.
Which Framework Are We Using?
We don't default to one framework. Here's exactly how we pick, and when each one makes sense.
| Framework | Best For | Skip It When |
|---|---|---|
| LangGraph | Complex state machines, auditability, regulated industries (healthcare, fintech) | Simple linear workflows where the overhead isn't worth it |
| CrewAI | Multi-role agent teams, fastest time-to-production for standard business workflows | Scenarios that need precise, granular state control |
| AutoGen / MS Agent Framework | Microsoft ecosystem, enterprise conversational agents, Azure-native stacks | Teams not already committed to Microsoft tooling |
| OpenAI Assistants API | Simple tool use, rapid prototyping, OpenAI-only workloads | Complex multi-agent orchestration or non-OpenAI model requirements |
AI Agent Projects by Afnexis
Real agents. Real clients. Real numbers. Not proof-of-concept screenshots.
FinanceLink Australia: Onboarding Automation
Document intelligence agent that extracts client data from uploaded PDFs, validates identity documents, cross-references internal databases, and pushes verified records to Salesforce. A process that took a team 3 hours now runs in under 5 minutes per client.
ShinyLoans: Automated Credit Decision Workflow
Multi-agent system that runs credit bureau pulls, bank statement analysis, fraud scoring, and preliminary approval logic without human intervention. Two analysts freed from routine decisioning. Approval times dropped from 48 hours to under 2 hours.
My Medical Records AI: Clinical Document Agent
HIPAA-compliant document intelligence agent that processes medical records, extracts structured clinical data, and populates patient profiles. Runs in an air-gapped AWS deployment with full audit logging. No PHI leaves the client's infrastructure.
RadShifts: Staffing Coordination Agent
Agentic scheduling system that matches open radiology shifts with qualified technicians based on certifications, location, and availability. Sends Slack confirmations, updates the staffing database, and flags conflicts. Replaced a manual process that ate 2+ hours of a coordinator's day.
Pricing & Timelines
Ranges based on 50+ real projects. LLM usage costs (tokens, API calls) are separate and vary by usage volume.
| Agent Type | What It Covers | Timeline | Starting At |
|---|---|---|---|
| Reactive Agent | Single task: FAQ bot, form processor, data extractor | 3 to 5 weeks | $20K |
| Contextual Agent | Multi-step workflow, short-term memory, API integrations | 5 to 8 weeks | $40K |
| Autonomous Agent | Planning logic, tool orchestration, decision-making | 8 to 14 weeks | $80K |
| Multi-Agent System | Orchestrated agent networks, complex enterprise workflows | 12 to 20 weeks | $120K |
Frameworks & Models We Use
LangGraph
OpenAI GPT-4o
Anthropic Claude
Python
Hugging Face
AWS / Azure
Evaluating other options? See how Afnexis compares to Toptal, Turing, Arc.dev, and more →
What Is AI Agents?
AI agent development is the process of building autonomous software systems that use large language models (LLMs) to reason, plan, and take multi-step actions. Unlike traditional automation, AI agents handle ambiguous inputs, adapt to exceptions, and orchestrate tools and APIs without rigid rule-based logic. Common frameworks include LangGraph, CrewAI, and AutoGen.
Key Facts
- ✓57% of companies already have AI agents in production (Azumo, 2025)
- ✓Gartner projects 40% of enterprise apps will include AI agents by end of 2026
- ✓Average ROI from AI agents: 171%, with US enterprises averaging 192%
- ✓Agentic AI market: $8.5 billion in 2025, growing to $10.9 billion in 2026
Last updated: April 2026
AI Agents: Frequently Asked Questions
AI agents handle multi-step workflows that previously required human judgment: document extraction and processing, lead research and outreach, customer support triage, data entry from unstructured sources, API orchestration, and internal knowledge retrieval. If it involves reading, reasoning, and taking action, an agent can handle it.
Zapier follows rigid if-this-then-that rules. AI agents use language models to reason through ambiguous inputs, handle exceptions, make judgment calls, and adapt to new situations without pre-defining every path. An agent can read a PDF, extract meaning, decide what to do next, and execute. Zapier can't.
Reactive agents (FAQ bots, data extractors) start at $20,000 and take 3 to 5 weeks. Contextual agents with multi-step workflows and API integrations run $40,000 to $70,000 over 5 to 8 weeks. Autonomous agents with planning logic run $80,000 to $120,000. Multi-agent systems start at $120,000.
LangGraph for complex state machines, auditability, and regulated industries. CrewAI for multi-role agent teams where speed to production matters. AutoGen for Microsoft-native stacks. We pick the right one based on your constraints and don't default to whatever is trending.
Need AI Agents Help?
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Know Your Cost Before You Commit.
30 minutes with a senior engineer who's shipped 50+ projects. You'll get a realistic scope, timeline, and fixed price estimate. No sales pitch. The call is useful even if you don't hire us.
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