AI agent development for real work, not demos
We build AI agents that plan, use tools, and act across your systems, not chatbots that only answer questions. Our agents include a security analysis platform that failed less often on edge cases than the pipeline it replaced, a market intelligence agent that monitors six data dimensions, and a customer service agent that handles 90 percent of query volume at Eona.ae. Built with the Claude Agent SDK and LangGraph.
Engagement · Scope
Fixed price. Shipped in 14 to 16 weeks.
What you get
- Agents that act, not just answer
- Runtime validation of outputs
- Governed access to your systems
- Measured accuracy before launch
App
Model-agnostic by design
Swap or combine any model. Your app is never locked to one vendor.
Why Most AI Agents Never Leave the Demo
The gap between an agent demo and a production agent is engineering:
01
Brittle on Real-World Variation
Demo agents work on the happy path. Production inputs vary in ways a prompt cannot anticipate, and sequential LLM pipelines fail quietly when they do.
02
No Validation of Outputs
An agent that acts on unverified conclusions creates work instead of removing it. Outputs need checking against reality before anyone trusts them.
03
No Access to Your Systems
An agent that cannot read your repositories, CRM, or data sources can only talk about work. Useful agents need governed access to real tools.
04
Unclear Where Agents Help
Agents are the wrong tool for many jobs. Without clear scoping, teams spend months building an agent for work a script would do better.
Agentic AI Development ServicesHow we build agents that survive production, the engineering behind our delivered systems:
1 · AI Agent Workflows
Adaptive, Not Sequential
- Agent workflows that adapt to input variation instead of failing on it
- Delivered: fewer edge-case failures than the sequential LLM pipeline it replaced, in the client's own tests
- Graph-based system modeling so agents understand what they are working on
- Built with the Claude Agent SDK and LangGraph
2 · Tool Use and System Access
Agents That Can Act
- Governed connections to your repositories, CRMs, and data sources
- Model Context Protocol integrations for standardized tool access
- Role-based permissions so agents only touch what they should
- Full audit logging of every agent action
3 · Validation and Reliability
Trust Through Verification
- Runtime validation of agent outputs before anyone acts on them
- Delivered: false positives cut via runtime checks in security analysis
- Evaluation sets with ground-truth answers, measured before launch
- Failure handling that recovers instead of silently stopping
4 · Autonomous Monitoring Agents
Watching So Your Team Does Not
- Agents that monitor sources continuously and surface what matters
- Delivered: six intelligence dimensions monitored autonomously for BWN
- Daily alert quotas so signal stays ahead of noise
- Multi-channel delivery to Teams, email, and dashboards
What we measureset against your baseline in discovery
Every engagement is scoped to a fixed price and an outcome you can measure, not billable hours.
Reliability
Fewer
edge-case failures than the old pipeline
Delivery
14wk
kickoff to production
Monitoring
6
data dimensions monitored autonomously
THE EDGEFIRM DIFFERENCE
Unlike agent framework demos:
- Validation and failure handling built in
- Measured accuracy before launch
- Engineered for input variation
Unlike large consultancies:
- 14 weeks to production on our fastest agent build
- Fixed-price engagements
- Engineers run the project end to end
Unlike chatbot vendors:
- Agents that act across systems, not just chat
- Your infrastructure, your code
- No per-seat or per-conversation fees
Common Questions About AI Agent Development
Workflow agents are AI agents that run a business process from start to finish, such as checking an order, updating a record, and notifying a customer. They follow the rules of your process and pass hard cases to a person. We design them as a graph of steps, so each step can be tested and logged.
Agentic AI development is building software systems where an AI model plans a task, chooses and uses tools, observes the results, and adjusts its approach, instead of following a fixed script. It differs from standard AI integration in that the system decides the steps at runtime. We build these systems with runtime validation, evaluation sets, and audit logging so you can measure and control what the agent does.
Software agents that plan, use tools, and act across systems. From our delivered work: security analysis agents that scan enterprise repositories and validate findings at runtime, autonomous market intelligence agents monitoring six data dimensions, customer service agents that check orders and take actions, and knowledge agents that answer onboarding questions from your documentation.
A chatbot answers questions. An agent takes actions: it reads your systems, runs tools, validates what it finds, and acts on multi-step workflows. The Eona agent does not just say where an order is, it checks the delivery system and pushes updates. The security agent does not just flag code, it validates the finding at runtime before reporting it.
Only with engineering that most demos skip. Our security analysis platform failed less often on edge cases after we replaced sequential LLM pipelines with adaptive agent workflows, and runtime validation cut false positives. Gartner expects over 40% of agentic AI projects to be canceled by the end of 2027, so scoping matters. We test every agent against an evaluation set with known answers before launch, and we will tell you when an agent is the wrong tool.
Our fastest production agent system, the Harbinger market intelligence platform, went from kickoff to production in 14 weeks. Most engagements run 14 to 16 weeks with a working demo every week. Published agency guides give 5 to 16 weeks for simple agent builds.
Engagements run $75,000 to $175,000 fixed price, depending on agent complexity, how many systems it must access, validation requirements, and scale. You own all the code, with no per-seat or per-conversation fees.
Yes. We connect agents to your systems through governed tool use and Model Context Protocol integrations, with role-based permissions so an agent only touches what it should. The Eona agent reads and acts on live order and delivery data; the same pattern applies to a CRM, ticketing system, or internal tool, with every action logged.
Every agent we build includes runtime validation, so outputs are checked before anyone acts on them, not after. Our security analysis platform cut false positives this way. When an agent is still unsure or a case falls outside what it was tested for, failure handling routes it to a person instead of acting on a guess, and the action is logged so you can see exactly what happened.
Process automation runs a defined workflow: extract this field, check this rule, send this update. An agent plans its own steps and adjusts when the input varies, choosing which tools to use and in what order. If your workflow is mostly fixed with a few exceptions, process automation is usually the better fit and the faster build. If the task needs judgment at each step, an agent is the right tool. We will tell you which one fits during scoping.
No. We build with the Claude Agent SDK and LangGraph because that is what we have hardened in production, but the architecture and evaluation patterns are not tied to one vendor's framework. If you already have agent work started on another stack, we will assess it during scoping rather than assume a rebuild.
Go Deeper:
Custom LLM Applications
The RAG and retrieval foundation most agents are built on.
AI Automation Agency
The business view of agent-driven automation.
Guide: The Reality of AI Agents in 2026
What agents can and cannot do in production today.
Guide: What is MCP for Business
How the Model Context Protocol lets agents connect to enterprise tools.
Get AI live in months, not years.
Book a 30-minute strategy call with the founders who will write your code.
