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AI Agent Development

AI Agent & Multi-Agent Development

An agent that can act is only useful if you can trust it. I build agentic systems on LangGraph, CrewAI and the Model Context Protocol with explicit state, checkpointed memory, tool permissions and human-in-the-loop approval gates. Automation can then scale without handing an LLM unsupervised access to anything that matters.

At a glance

  • Real actions, not just answers
  • Human-in-the-loop control
  • Durable state and memory
  • Pluggable tools via MCP
Book a free discovery call See related case studies
Outcomes

What you get from this engagement.

  • 01

    Real actions, not just answers

    Agents that search, query, write and update across your systems.

  • 02

    Human-in-the-loop control

    Graph-level approval gates before sensitive actions, not just prompt instructions.

  • 03

    Durable state and memory

    Checkpointed workflows that survive restarts and resume exactly where they stopped.

  • 04

    Pluggable tools via MCP

    New capabilities connect as MCP servers instead of bespoke integrations.

Deliverables

Scope, made concrete.

  • Agent architecture and tool-permission design
  • LangGraph or CrewAI implementation
  • Custom MCP servers and clients for your tools and data
  • Human-in-the-loop approval workflows
  • Tracing and observability with LangSmith or Langfuse
  • Deployment, documentation and handover
Use cases

Where it delivers value.

  • Research and analyst agents
  • Cloud and DevOps operations agents
  • Document-processing crews
  • Sales, CRM and back-office agents
  • Internal IT and helpdesk agents

Technology

  • LangGraph
  • LangChain
  • CrewAI
  • Model Context Protocol
  • Claude Agent SDK
  • OpenAI
  • Amazon Bedrock
  • LangSmith
  • Langfuse
  • Python
  • FastAPI
Proof

Related case studies.

Real systems delivered with this approach, with the constraints and results that came with them.

agent-run · langgraph + mcp
HITL

  1. Assess Q3 exposure for portfolio Alanggraph · state

  2. web_search("Q3 earnings guidance")mcp · ok

  3. read_docs("risk-policy.pdf")mcp · ok

  4. get_financial_data(account="A-17")awaiting approval

    Sensitive tool. Execution paused by a graph interrupt.

    ApproveReject

state checkpointed · resumes exactly here after a decision

Illustrative interface · client data never shown
01MCP Agent Platform

Agentic Orchestration Platform with LangGraph and MCP

A stateful agent platform on LangGraph that reaches live tools through the Model Context Protocol, remembers context across sessions and pauses for human approval before any sensitive action runs.

Approval gates on sensitive actions
HITL
Checkpointed multi-session memory
Durable
Plug-in tool federation
MCP
Read case study
cloudops-agent · amazon bedrock
Confirm-first
Create a private S3 bucket for invoices in Ireland, with versioning.

I’ll create this resource. Please confirm:

Bucket
invoices-archive-eu
Region
eu-west-1
Access
Private, public access blocked
Versioning
Enabled
Confirm this change? (yes/no)Yes, createNo

boto3 call runs only after an explicit yes · claude 3.5 sonnet on bedrock

Illustrative interface · client data never shown
02CloudOps Agent

Natural-Language Cloud Operations Agent on Amazon Bedrock

A chat-driven AWS operations agent on Amazon Bedrock that lets non-engineers create, inspect, update and delete cloud resources in plain language, with validation and confirm-before-execute guardrails on every change.

Changes require explicit approval
100%
Full resource lifecycle in plain language
CRUD
Claude 3.5 Sonnet on AWS
Bedrock
Read case study
Fintech & Capital Markets5 stages
  1. 01Ingest
  2. 02Extract
  3. 03Summarize
  4. 04Structure
  5. 05Deliver

Stage 1, Ingest: PDF and transcript extraction. Stage 2, Extract: Figures, guidance, signals. Stage 3, Summarize: Specialized summarization agent. Stage 4, Structure: Report-structuring agent. Stage 5, Deliver: Client-ready report.

03Earnings Intelligence Crew

Financial Intelligence Multi-Agent Pipeline

A CrewAI multi-agent pipeline that reads earnings-call transcripts and financial PDFs, splits the analysis across specialized agents and produces structured, client-ready reports.

Specialized CrewAI roles
Multi-agent
Unstructured financial inputs
PDF + calls
Structured client output
Report-ready
Read case study
Productivity4 stages
  1. 01Research
  2. 02Score
  3. 03Draft
  4. 04Review

Stage 1, Research: Live web search. Stage 2, Score: Topic relevance scoring. Stage 3, Draft: Posts, comments, cover letters. Stage 4, Review: Human edits and publishes.

04Content Copilot

Human-in-the-Loop Content & Career Agent

A local-first, three-agent assistant that drafts LinkedIn posts, comments and tailored cover letters and scores topics against live web search. Every publish and apply action is left to a human by design.

Cooperating agents
3
Topic scoring
Web-grounded
Automated publishes, by design
0
Read case study
How we work

A delivery process built for zero surprises.

Clear milestones, working demos every week and measurable quality gates, so you always know what's done, what's next and what it costs.

  1. 01

    Discover

    A free 30-minute call to understand the problem, the data and what success looks like.

    You getClear problem statement and success metrics

  2. 02

    Architect

    A written technical plan: architecture, model and vector-store choices, risks, milestones and a fixed quote.

    You getArchitecture document and milestone plan

  3. 03

    Build

    Iterative delivery in short milestones with working demos, so you see progress every week, not at the end.

    You getWorking increments you can test

  4. 04

    Evaluate & harden

    Measured against an evaluation set, then hardened with guardrails, access control, error handling and load testing.

    You getEvaluation report and test suite

  5. 05

    Deploy & support

    Dockerized deployment to your cloud, documentation and handover, with optional ongoing support and improvement.

    You getProduction system plus documentation

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FAQ

AI Agent Development: common questions.

What is the Model Context Protocol (MCP)?

MCP is an open standard for connecting AI models to tools and data. Instead of hand-coding every integration, each capability is exposed as an MCP server, so you can add tools without rewriting the agent and reuse them across AI clients.

How do you stop agents from taking harmful actions?

With architecture, not hope. Tools get least-privilege permissions, mutating actions go through validation, and sensitive steps are behind real interrupts that wait for a human to approve or reject before execution continues.

LangGraph or CrewAI: which is better?

LangGraph is my default for production agents that need explicit state, branching and human approval. CrewAI is excellent for role-based crews that split one analytical job across specialists. I have shipped both and choose based on the workflow.

Can agents work with our existing systems?

Yes. Agents can call your REST APIs, databases, CRMs, cloud providers and internal tools, usually wrapped as MCP servers or typed tools with clear permissions.

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Free discovery call. You'll leave with a clear view of the architecture, risks and next steps, whether or not we work together. I reply within 24 hours.

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