Agentic AI & Context Engineering
Custom AI agents that do the work, on cloud models or local LLMs
Agentic AI is the shift from chatbots that answer to agents that act. TrendLab Analytics designs and ships custom AI agents for small and mid-size businesses: systems that read your data, plan the steps, call your tools and finish the job, whether that is closing the books, triaging inbound documents, exploring a new dataset or drafting the weekly report. We run them on cloud models such as Claude, OpenAI and Gemini, or on local LLMs on your own hardware when the data cannot leave the building. Based in Atlanta, Georgia and serving clients nationwide, the practice is led by a principal consultant who leads enterprise AI adoption at a Fortune 500 financial institution and works daily in the agentic toolchain: Claude Code, Codex CLI, GitHub Copilot CLI, Gemini CLI and localized LLMs on Windows, macOS and Linux.
Who this is for
- •Owners and operators who have tried ChatGPT and want the next step: an agent that owns a workflow end to end
- •Regulated or privacy-sensitive teams (healthcare, finance, legal) that need AI on local models with no data leaving their network
- •Analytics and engineering teams that want agentic engineering practices, so AI writes, tests and maintains real code and pipelines safely
- •Leaders who want their people trained to work with agents instead of watching the tools sit unused
What you get
- •Custom agents with tool access to your databases, files, email, CRM and APIs, scoped to a job and measured on completion
- •Context engineering: the retrieval, memory, instructions and guardrails that make an agent reliable on your data instead of generic. This is where most AI projects succeed or fail.
- •Cloud or local deployment: Claude, OpenAI or Gemini APIs for capability, or local LLMs (Ollama, llama.cpp, vLLM) on your servers or workstations for privacy and cost control
- •Agentic engineering for your codebase and pipelines: AI-assisted development with Claude Code, Codex CLI, Copilot CLI and Gemini CLI, with review gates, tests and evals so speed does not cost you correctness
- •Agent evaluations and monitoring: a test set of real cases, pass rates you can read, and alerts when behavior drifts
- •Enterprise AI adoption enablement: playbooks, prompts and hands-on training so your team runs and extends the agents themselves
Results from similar work
10,000+
documents processed by rules and models
Conditional-logic document generation at scale, the same foundation our document-reading agents are built on.
4
agentic CLIs in daily use
Claude Code, Codex CLI, GitHub Copilot CLI and Gemini CLI, applied to real analytics and engineering work, not demos.
How it works
- 1.Discovery call to pick the one workflow where an agent pays off fastest and to decide cloud vs local based on your data policy
- 2.Context engineering pass: gather the documents, rules, examples and tool definitions the agent needs, and build the eval set
- 3.Build the agent with a human-in-the-loop checkpoint, run it against the eval set, then widen its autonomy as pass rates prove out
- 4.Deploy with monitoring, hand off with training, and add the next workflow
Timeline
A first production agent on a single workflow usually ships in 3 to 6 weeks. Local-LLM deployments add a week for hardware and model selection.
Pricing
Fixed-scope quotes after the discovery call. Local deployments trade a one-time hardware and setup cost for near-zero per-request cost, and we model that trade-off for you before you commit.
Tools
Claude API and Claude Code · OpenAI API and Codex CLI · Gemini API and Gemini CLI · GitHub Copilot CLI · Local LLMs: Ollama, llama.cpp, vLLM · Python · AWS · Windows, macOS and Linux
Agentic AI & Context Engineering questions answered
Straight answers. If yours is not here, the discovery call is free.
What is agentic AI, in plain terms?
A chatbot answers a question. An agent is given a goal, plans the steps, uses tools such as your database, files and email, checks its own work and finishes the task. Agentic AI is building those systems so they run reliably inside a business.
What is context engineering and why does it matter?
Context engineering is deciding exactly what an agent sees at each step: which documents, rules, examples, memory and tool definitions, in what order, and what it is not allowed to do. Two agents on the same model can be useless or excellent depending on this. It is the core of our work.
Can the AI run locally so our data never leaves our network?
Yes. We deploy local LLMs on your own servers or workstations for privacy, compliance and cost control, and we tell you honestly when a task needs a larger cloud model.
What is agentic engineering?
Using AI agents as part of how software and pipelines get built and maintained: agents write and test code under review gates, with evals that prove correctness. We set this up for teams so they ship faster without lowering the bar.
How do you keep an agent from making things up or doing damage?
Scoped tool permissions, human-in-the-loop checkpoints until the eval pass rate earns more autonomy, an eval set built from your real cases, and monitoring after launch.
Can you train our team to use these tools?
Yes. Enterprise AI adoption enablement is part of the service: playbooks, prompt and context libraries, and hands-on sessions so the agents keep delivering after we leave.
Start with a free 30-minute call
Tell us the process, report or dataset that is costing you the most time. You leave with a clear next step, whether or not that is us.
Other services: Business Process Automation · Analytics & Dashboard Reporting · AI & Machine Learning · Data Engineering