Agent Engineering
Designing, evaluating, and orchestrating non-deterministic AI systems with explicit specification, feedback loops, and operational guardrails.
Agent Engineering exists because models don't read minds and production constraints are real. It is the discipline of specifying, designing, building, and orchestrating systems that behave reliably even when every input is an edge case. The work is about making non-deterministic AI dependable in the real world.
Core Disciplines
Engineering
Engineering
Engineering
Engineering
Engineering
What is Agent Engineering?
Agent Engineering is the discipline of designing, evaluating, and orchestrating non-deterministic AI systems with explicit specification, feedback loops, and operational guardrails.
Agentic AI emerges when context, memory, evaluation, orchestration, tooling, and infrastructure are intentionally designed as a modular, future-proof system. Intelligent specification, human-in-the-loop oversight, and strategic orchestration are foundational.
We focus on what actually works in production: real constraints, real tradeoffs, and real systems.
Core Engineering Themes
Code Engineering
How agents write, modify, and reason over code in real workflows
Eval Engineering
Behavioral testing, evaluation frameworks, and reliability guardrails
Memory Engineering
State, retrieval strategies, and personalization over time
Context & Skills Engineering
Context construction, compression, grounding, and MCP workflows
Harness Engineering
Execution environments, tools, policies, and sandboxes
Multi-Agent Engineering
Coordination, task decomposition, and agent collaboration
The Agent Engineering Mindset
Non-Determinism
Embrace unpredictability as a feature, not a flaw
- Design for variance, retries, and fallbacks
- Measure behavior, not just outputs
Intelligent Specification
Models do not read minds
- Explicit specs, constraints, and success criteria
- Better planning yields better agents
Every Input is an Edge Case
Ship to learn, not to be perfect
- Production feedback loops over static tests
- Real-world inputs define reliability
Strategic Orchestration
Allocate compute and review efficiently
- Budget-aware routing and escalation
- Human review where it matters most
The Reviewer Framework
Validation loops and automated gates
- Reviewer agents for quality control
- Automated PR gates and safety checks
Agent Networking
Agents coordinate and communicate
- Parallel and sequential workflows
- Prevent conflicts and overlap
Advanced Program Tracks
Agent Optimization
Optimize agents across prompts, RAG, protocols, memory, and context
Agent Experience (AX)
Design machine-readable interfaces and feedback loops that shape AX
Agentic Coding
How development evolves as agents become first-class SDLC contributors
Agentic Business Models
Rethink SaaS and explore models for agent-native businesses
The Agent Engineering Manifesto
Models Don't Read Minds
Agent engineering starts with explicit specification and clear intent
Agentic Systems Are Modular
Context, memory, evaluation, orchestration, and tooling must be designed together
Guardrails + Feedback Loops
Operational oversight and iterative feedback make agents production-ready
Why Agent Engineering is Different
Determinism
Predictable outputs with fixed logic
Non-deterministic behavior that must be managed and observed
Specification
Requirements live in code and tickets
Explicit specs and guardrails define agent behavior
Orchestration
Single systems and linear workflows
Strategic routing across tools, models, and reviewers
Evaluation
Predefined test cases
Behavioral evals and continuous feedback loops
The Agent Engineering Ecosystem
Key domains shaping production-grade agentic systems
Agentic Coding
Coding agents, pair programming, code review, and spec-driven workflows
Agent & Tooling
Frameworks, orchestration platforms, SDKs, and multi-agent systems
Models & Foundations
Frontier model providers, developer platforms, and applied AI tooling
Agent Dev Tools
Testing tools, evaluation frameworks, and MCP tooling
AgentOps & Traceability
Tracing, observability, model serving, inference engines, and sandboxes
Enterprise & Security
IAM for agents, tool-use guardrails, and compliance automation
Who Builds Agent Engineering
Agent engineering is a cross-functional discipline. Builders, ML engineers, platform teams, product leaders, and founders all contribute to making agents reliable in production.
Software Engineer / ML Engineer
Writes deterministic code for fixed logic and builds ML models
Agent Engineer responsibilities: Writing prompts and building tools for agents to use, tracing why an agent made specific tool calls, and refining the underlying models. Designs agent scaffolds with tools, memory, and reflection loops.
- Write prompts that drive agent behavior (often hundreds or thousands of lines)
- Build tools and APIs for agents to interact with
- Trace agent decision-making and tool call sequences
- Refine models and prompts based on production insights
Product Manager
Manages user stories, backlogs, and product roadmaps
Agent Engineer responsibilities: Writing prompts, defining agent scope, and ensuring the agent solves the right problem. Deeply understands the 'job to be done' that the agent replicates and defines evaluations that test whether the agent performs as intended.
- Write prompts that shape agent behavior and scope
- Define high-level intent and goal specifications
- Ensure the agent solves the right problem
- Define evaluations that test agent performance
Platform Engineer
Manages CI/CD pipelines, uptime, and infrastructure
Agent Engineer responsibilities: Building agent infrastructure that handles durable execution and human-in-the-loop workflows. Creates robust runtimes that handle durable execution, human-in-the-loop pauses, and memory management.
- Build agent infrastructure for durable execution
- Design human-in-the-loop workflow systems
- Create robust runtimes with memory management
- Develop UI/UX for agent interactions with streaming and interrupt handling
Data Scientist
Builds ML models, analyzes data, and creates predictive insights
Agent Engineer responsibilities: Measuring agent reliability and identifying opportunities for improvement. Building systems (evals, A/B testing, monitoring) to measure agent performance and reliability, and analyzing usage patterns and error analysis.
- Build evaluation systems to measure agent performance
- Run A/B tests and monitor agent reliability
- Analyze usage patterns and error analysis
- Identify opportunities for improvement based on production data
The best teams treat specification, evaluation, and orchestration as shared responsibilities. They combine human oversight with automated guardrails so agents stay aligned as they scale.
Production-Ready Agentic AI
Building agentic AI for production is fundamentally different from traditional software. Every input is an edge case, and reliability comes from explicit specification, feedback loops, and operational guardrails.
Agent Engineering is the differentiator as models become more capable and commoditized. The teams that win will orchestrate context, memory, evaluation, and tooling as a unified system.