AI Agents
Autonomous systems that work while you sleep.
We engineer production-grade AI agents that plan, reason, use tools, and complete multi-step workflows autonomously — reducing manual overhead and creating compounding operational leverage across your business.
Beyond Chatbots — Agents That Actually Do Things
A chatbot answers questions. An agent takes actions. The distinction matters enormously — because the real leverage in AI is not in generating text, it is in automating the workflows that currently consume your team's most expensive hours.
CodeWingz builds AI agents that are connected to your systems: they can read and write to databases, call external APIs, browse web sources, execute code, send emails, create documents, and trigger actions in your existing software stack. They plan multi-step tasks, handle failures gracefully, and know when to escalate to a human operator.
We use established agentic frameworks (LangGraph, AutoGen, CrewAI) and build custom orchestration where needed. Every agent ships with full audit logging, rate limiting, cost controls, and a human-in-the-loop escalation path — because autonomous does not mean ungoverned.
Service Inclusions
Multi-Step Task Execution
Agents that decompose complex goals into executable sub-tasks, track progress across steps, handle tool failures, and retry intelligently — without human intervention.
Tool & API Integration
Custom tool definitions connecting your agent to any system: internal databases, Salesforce, Jira, Slack, email, web browsers, code executors, and file systems.
Multi-Agent Orchestration
Specialist sub-agents (researcher, writer, validator, executor) coordinated by a supervisor agent for complex workflows that benefit from parallel processing.
Audit & Observability
Complete trace logging of every agent decision, tool call, and output. LangSmith or custom observability dashboards for monitoring agent behaviour in production.
Guardrails & Cost Controls
Token budget limits, action whitelists, sandboxed tool execution, and human approval gates for high-stakes actions like sending emails or making purchases.
Custom Memory Systems
Short-term working memory, long-term episodic storage, and semantic retrieval so agents remember context across sessions and accumulate operational knowledge over time.
A Process Built for Clarity
No black boxes. No surprise invoices. Every project at Codewingz follows a disciplined four-phase process designed to reduce risk and maximise value at every stage.
Workflow Mapping
We document your target workflow step-by-step, identify decision points, map required tools and data sources, and define success criteria for autonomous completion.
Tool & Integration Design
We design the tool schema — every API, database query, and action the agent needs — and build secure integrations with authentication and rate limiting.
Agent Architecture
We select the orchestration approach (single agent, supervisor + sub-agents, parallel crew) and implement the reasoning loop, memory system, and error handling.
Testing & Red-Teaming
Adversarial testing: edge cases, tool failures, ambiguous inputs, and attempts to jailbreak the agent into unintended actions. Evaluation against real workflow scenarios.
Supervised Rollout
Initial deployment with human-review mode enabled. We monitor every agent run, refine decision logic based on real cases, and progressively increase autonomy.
Full Autonomous Operation
Agent operates fully autonomously with alerting for anomalies. Monthly performance reviews and prompt/logic updates as your workflows evolve.
The Tech Stack
We select technologies based on performance, scalability, and long-term maintainability, not trends.
LangGraph
Building stateful, multi-actor applications with LLMs.
CrewAI
Collaborative role-playing AI agents.
Playwright
Reliable end-to-end testing for modern web apps.
Python
The language of AI and data science.
PostgreSQL
The world's most advanced open source database.
Docker
Containerization for consistent environments.
Redis
In-memory data structure store.
FastAPI
Modern, fast web framework for Python.
Real-World Impact
PropManage Pro
The Challenge
“A property management SaaS company was spending 3 FTE hours per day manually processing rental applications: collecting documents, running credit checks via API, cross-referencing landlord criteria, and drafting decision letters. The process was slow, inconsistent, and bottlenecking their sales cycle.”
The Solution
We built a multi-agent system with a coordinator agent routing applications to specialist sub-agents: a document extraction agent (OCR + structured parsing), a verification agent (credit API + identity check), a scoring agent (configurable landlord criteria), and a communications agent (approval/rejection emails with explanation). Human review was gated only for edge cases scoring within 10 points of the landlord's threshold.
Key Performance Indicators
Common Inquiries
Everything you need to know about our specialized services.
Which Workflow Should Be Running Itself?
Describe the process your team repeats most often — we will tell you whether an agent can own it, and what that would look like in production.