Agentic engineering
Agentic engineering is the disciplined end of the AI-coding spectrum, where AI acts as a powerful implementation engine inside carefully designed systems of constraints, tests, and feedback loops, with humans retaining oversight over architecture, correctness, and quality. Andrej Karpathy introduced the term in early 2026 to name the disciplined counterpart to vibe coding, after his original "vibe coding" framing was applied so broadly it lost meaning.
The spectrum
AI-assisted coding runs from vibe coding through structured AI-assisted coding to agentic engineering. The key differentiator is not whether you use AI but how much structure, verification, and human judgment surrounds the output.
Vibe coding | Structured AI-assisted coding | Agentic engineering | |
|---|---|---|---|
Intent specification | Casual prompts | Detailed prompts with examples | Formal specs, architecture docs, memory files |
Verification | "Does it seem to work?" | Manual spot-checking | Automated test suites, CI/CD gates, LM judges |
Codebase understanding | Minimal, may not read generated code | Selective review of critical paths | Comprehensive architecture review with AI on implementation details |
Error handling | Copy-paste errors back to AI | Developer diagnoses root cause, AI fixes | Agents self-diagnose within bounds, humans handle architectural issues |
Appropriate scope | Prototypes, scripts, hackathons | Features in established codebases | Production systems, team-scale |
Risk profile | High, acceptable for disposable work | Moderate, human judgment at key checkpoints | Low, systematic verification at every stage |
Tests and evals as the dividing line
The single biggest differentiator is how outputs get verified. Tests verify the deterministic parts — a function given this input produces that output. Evals verify the non-deterministic parts: did the agent take the right trajectory of steps, choose the right tools, produce a response that meets the quality bar? Evals are checked by labelled datasets, scoring rubrics, and LM judges. Without both, the practice is always vibe coding, however sophisticated the prompts. See automated testing.
The developer as factory manager
In agentic engineering the developer’s primary output is not code but the system that produces code — specifications and context, agents that translate specs into implementation, tests and quality gates, feedback loops that route failures back for correction, and guardrails that constrain agents to safe behaviour. Writing the specification before any code is generated is the substance of spec-driven development. A factory manager designs the assembly line and ensures quality control rather than assembling every widget by hand. Success comes from giving agents success criteria rather than step-by-step instructions. See agent harness and loop engineering.
The economics
Agentic engineering is high CapEx — an upfront investment in API schemas, deterministic test suites, and structured context — but low OpEx. The AI operates within a governed factory, so its output is structurally sound, pre-tested, and aligned with standards, and the marginal cost of shipping and maintaining a feature drops dramatically. This inverts vibe coding's low-CapEx/high-OpEx profile.
Context engineering is a financial lever in the token economy: a dense, high-signal payload — a precise AGENTS.md and architectural guardrails — raises first-pass success and avoids the costly trial-and-error loops of vibe coding. See context engineering and large language model.
Context engineering as the bridge
The shift from prompt engineering to context engineering is the bridge between vibe coding and agentic engineering. Models need the same context a skilled human developer would need, not cleverly worded instructions.
See also
- Vibe coding
- Agent
- Agent harness
- Context engineering
- Loop engineering
- Developer experience (DX)
- Automated testing
- Large language model (LLM)
References
- The New SDLC With Vibe Coding, Addy Osmani, Shubham Saboo & Sokratis Kartakis (2026): From ad-hoc prompting to agentic engineering; introduces the spectrum and the factory model.