# Agentic AI Development & AI Harnesses

> Agentic AI development - agentic workflows, multi-agent systems, and the AI harness (planning, tools, memory, evals) that makes autonomous agents reliable in production.

Agentic AI is the shift from single prompts to systems that plan, act, and adapt. I build agentic workflows and the AI harness around them - the planning, tool execution, memory, and evaluation layers that turn an autonomous loop into something you can actually depend on.
The hard part of agentic development is not the model; it is the harness. Reliability comes from how you decompose the goal, scope the tools, verify each step, and recover from failure.

## Key takeaways

- Agentic AI is the shift from single prompts to systems that plan, act, and adapt toward a goal.
- The hard part is the harness - planning, tool execution, memory, and evaluation - not the model.
- Single-agent and multi-agent designs both live or die on how each step is verified and recovered.
- Autonomy is added deliberately, scoped to exactly where it earns its keep.

## What I build

- **The AI harness** - A deliberate scaffold around the model - task decomposition, tool routing, structured outputs, retries, and verification - so autonomous behavior stays bounded and observable.
- **Agentic workflows** - Goal-driven pipelines where the system decides the next step, calls tools, and self-corrects, instead of following a brittle hard-coded script.
- **Multi-agent orchestration** - Specialist agents that fan out, verify each other, and synthesize results - used where one context window or one perspective is not enough.
- **Evaluation & observability** - Eval suites, tracing, and adversarial checks so you can prove an agentic system works before and after every change.

## What "agentic" really means

An agentic system is given a goal rather than a script. It plans an approach, chooses tools, executes steps, observes the results, and adjusts - looping until the goal is met or a guardrail stops it. That autonomy is powerful and, without a harness, unpredictable.
My work focuses on the harness: the control structure that makes autonomy safe. Decompose the task, constrain the tool surface, validate every output, verify with independent checks, and you get a system that earns trust.

## Where multi-agent beats single-agent

Not every problem needs multiple agents. But for broad research, large migrations, or work that benefits from independent verification, a fan-out of specialist agents - each with a narrow job - outperforms one overloaded agent. The pattern is decompose, run in parallel, verify adversarially, then synthesize.
I design these topologies pragmatically: as many agents as the task warrants, with verification built in so confidence is earned, not assumed.

## Stack & tooling

LangGraph, Claude Agent SDK, OpenAI SDK, MCP, Eval harnesses, Tracing

## Frequently asked questions

### What is agentic AI?

Agentic AI describes systems that pursue a goal autonomously - planning, using tools, and adapting based on results - rather than producing a single one-shot response. The reliability comes from the harness wrapped around the model.

### What is an AI harness?

An AI harness is the engineering scaffold around a model: task decomposition, tool routing, memory, structured outputs, retries, verification, and evals. It is what turns a capable model into a dependable agentic system.

### When should I use a multi-agent system?

Use multiple agents when the work is broad enough to decompose, benefits from parallelism, or needs independent verification - for example large audits, migrations, or research. For narrow tasks, a single well-scoped agent is simpler and cheaper.

## Related capabilities

- [Custom AI Agent Development](https://adrees.dev/ai-agents)
- [RAG Systems & Retrieval-Augmented Generation](https://adrees.dev/rag-systems)
- [AI Systems & AI Operating Systems](https://adrees.dev/ai-systems)

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