AI agent development for production, not demos
An AI agent development company builds software agents that plan, use tools, and act across your systems, not chatbots that only answer questions. We have shipped agents for security analysis that cut workflow edge-case failures by 80 to 90 percent, autonomous market intelligence monitoring six data dimensions, and customer service agents handling 70 percent of query volume. Built on the Claude Agent SDK, LangGraph, and patterns proven in production.
Why Most AI Agents Never Leave the Demo
The gap between an agent demo and a production agent is engineering:
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.
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.
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.
Unclear Where Agents Help
Agents are wrong for many jobs. Without honest scoping, teams spend months building agentic versions of things a script would do better.
How We Build Agents That Survive Production
The engineering behind our delivered agent systems:
Agentic Workflow Design
Adaptive, Not Sequential
- Agent workflows that adapt to input variation instead of failing on it
- Delivered: 80 to 90% fewer edge-case failures versus sequential LLM pipelines
- Graph-based system modeling so agents understand what they are working on
- Built with the Claude Agent SDK and LangGraph
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
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
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
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
Built on Production-Grade Agent Infrastructure
Agent Frameworks
- Claude Agent SDK
- LangGraph
- LangChain
- Model Context Protocol (MCP)
- Custom orchestration
Models & Reasoning
- Claude
- GPT-4
- Llama & Mistral
- RAG over your data
- Evaluation harnesses
Validation & Data
- Neo4j graph modeling
- Playwright runtime validation
- PostgreSQL
- Ground-truth eval sets
- Audit logging
Infrastructure
- TypeScript & NestJS
- Python & FastAPI
- Docker/Kubernetes
- AWS/GCP/Azure
- Teams / email delivery
Agents We Have Shipped
Security Analysis Agents
Verified Vulnerability Intelligence
Challenges
- • Diverse enterprise architectures break brittle analysis pipelines
- • Static tools and isolated LLM calls flood teams with false positives
- • Manual review cannot keep pace with large codebases
Our Solutions
- • Agent-driven workflows with graph-based system modeling
- • Runtime validation so only exploitable findings surface
- • Adaptive failure handling across multi-service codebases
Typical Results
- • 80 to 90% fewer workflow edge-case failures
- • False positives reduced via runtime validation
- • Scales across multi-service enterprise codebases
Illustrative outcomes from comparable deployments. Actual results depend on your data, scope, and use case.
AI Code Security Analysis Platform
"Agent-driven security analysis that ingests enterprise repositories and produces validated, false-positive-free vulnerability reports with code-level remediation guidance."
Read Full Case StudyHow We Deliver Production Agents
Scoping and Honest Fit Assessment
- Define what the agent must do, and what a script should do instead
- Map the systems and tools the agent needs to access
- Build the evaluation set with ground-truth answers
- Design permissions, audit, and failure behavior up front
Deliverable: Agent architecture, eval set, and a no-go call where agents are wrong
Core Agent Development
- Build the agent workflows and tool integrations
- Implement validation of outputs against reality
- Test against the evaluation set, not demos
- Weekly live demos of the agent working
Deliverable: Agent passing accuracy targets on the eval set
System Integration and Pilot
- Connect production systems with governed, role-based access
- Run a pilot on real workload alongside the manual process
- Tune failure handling and escalation from pilot data
- Add monitoring, audit logging, and quality dashboards
Deliverable: Pilot results measured against the manual baseline
Production Rollout
- Scale to full workload
- Train your team on oversight and overrides
- Document everything and hand over the code
- 30 days of post-launch support
Deliverable: Production agent system plus 30 days support
Transparent Pricing for AI Agent Development
Typical Investment Range
$75,000 - $175,000
Full project delivery in 14 to 16 weeks
Factors that affect pricing:
Agent Complexity
Single-purpose monitoring versus multi-step workflows with judgment
Tool and System Access
How many systems the agent must read, write, and act on
Validation Requirements
How outputs are verified before anyone or anything acts on them
Scale and Oversight
Workload volume and the human oversight model around the agent
What's Included:
Common Questions About AI Agent Development
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 most demos skip. Our security analysis platform cut workflow edge-case failures by 80 to 90 percent by replacing sequential LLM pipelines with adaptive agentic workflows, and reduced false positives through runtime validation. Every agent we ship is measured against an evaluation set with ground-truth answers before launch. We will also 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. Typical engagements run 14 to 16 weeks with weekly live demos throughout.
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.
Go Deeper:
Custom LLM Applications
The RAG and retrieval foundation most agents are built on.
Learn MoreAI Automation Agency
The business view of agent-driven automation, with measured ROI.
Learn MoreGuide: The Reality of AI Agents in 2026
Our honest take on what agents can and cannot do in production today.
Learn MoreGuide: What is MCP for Business
How the Model Context Protocol lets agents connect to enterprise tools.
Learn MoreReady to Transform Your Business with AI Solutions?
Schedule a free strategy call to discuss your project and get a custom AI implementation roadmap.
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