5 Concepts for Coding With AI Agents [2026]

5 Concepts for Coding With AI Agents in 2026 (IBM's Framework, Stress-Tested)

Coding with AI agents is the practice of handing a software task to an autonomous, LLM-driven system that plans, writes, executes, and revises code across multiple steps, rather than autocompleting your next line the way an assistant does. In October 2026 that distinction is the whole ballgame. The concepts for coding with AI agents have hardened into an actual discipline, and IBM just put a name and a number on it.

The source is a video from IBM Technology, Cedric Clyburn walking through "5 Concepts to Know for Coding with AI Agents." It's a clean, quotable framework. It's also, in places, too clean. I run a seven-agent pipeline that writes and ships this blog, so I've watched most of these concepts break in ways a slide deck will never tell you about. This post does three things no one else has bothered to: it pairs each concept with real code, it says plainly where the framework holds up and where it oversimplifies, and it goes deeper on evaluation than any vendor explainer will.

One reason the framing matters _now_: the plumbing moved. The Model Context Protocol (MCP), the standard underneath the "tool use" concept, was donated by Anthropic to the Agentic AI Foundation under the Linux Foundation in December 2025 after OpenAI and Google DeepMind adopted it. Any 2026 discussion that still treats tool use as a novelty is a year stale.

Key takeaways:

• Coding with AI agents means autonomous multi-step execution, not autocomplete. If a human approves every line, you have an assistant, not an agent.
• IBM's five concepts (context, tool use, memory, orchestration, evaluation) are the right nouns, but the framework under-weights the two that actually bite in production: evaluation and security.
• MCP becoming a cross-vendor standard in December 2025 means tool use is now table stakes, not a differentiator.
• Deterministic checks catch more agent mistakes than a smarter review model. I have the incident log to prove it.
• The minimum viable setup for a small team is one agent, strong context hygiene, and a real eval harness. Orchestrating a swarm of agents first is almost always premature.

What IBM means by "coding with AI agents" (vs. a plain assistant)

Here's the thing nobody's saying clearly enough: "AI coding assistant" and "AI coding agent" are different products that happen to share a chat box.

GitHub Copilot launched in June 2021 as autocomplete. You type, it predicts, you accept or reject. The control flow is yours. An AI coding agent inverts that. You state a goal ("migrate this module off the deprecated SDK, run the tests, fix what breaks"), and the agent owns the loop: it reads files, writes changes, runs commands, reads the output, and decides what to do next. The LLM drives the control flow.

The standard definition of an AI agent names four attributes: goal-directed behavior, external tool use, the ability to act on an environment, and autonomous multi-step execution, with memory, planning, and orchestration as the supporting architecture. IBM's five concepts map almost one-to-one onto that architecture, which is why the framework is useful even if you never watch the video.

Here's the split at a glance:

If a human has to approve every edit, you built an assistant with extra steps. Agency is the whole point, and the whole risk.

Then there's the cultural backdrop: the vibe coding backlash. The term, coined by Andrej Karpathy in February 2025, became Collins Dictionary's Word of the Year for 2025. Then critics hammered it for producing unaccountable, unmaintainable, security-hole-ridden code. IBM's framework is implicitly a rebuttal to vibe coding. The whole pitch is: agency without concepts is just vibes at scale. On that, I agree completely.

Concept 1: Context Engineering

Context engineering is the practice of deciding what information the agent sees at each step: which files, which docs, which prior outputs, and in what order, so the model reasons over signal instead of noise. It's the successor to prompt engineering, and for agentic coding it matters far more.

The reason is a failure mode called context rot. Shove 180K tokens of repo into the window and the model doesn't get smarter. It gets distracted. Retrieval quality degrades, the agent re-reads files it already understood, and token costs balloon. Good context engineering is subtractive: you give the agent a curated slice, often via retrieval-augmented generation (RAG), not the whole tree.

In practice this is a retrieval step that runs before the agent plans. A semantic search over your vector embeddings pulls the five or so chunks most relevant to the task, and only those get stitched into the working prompt. The agent reasons over a few thousand tokens of targeted context instead of a quarter-million tokens of noise.

The cost side is where this gets concrete. Based on the LLM pricing data I maintain at kunalganglani.com/llm-prices and the cost calculators I built at /tools, per-token price comparisons mislead until you add cache-hit and retry assumptions, and a bloated context window quietly wrecks both. A 180K-token window you re-send uncached on every step is the fastest way I know to turn a $0.10 task into a $3 one. Curated context isn't only about accuracy. It's the cheapest knob you have.

IBM gets this right by elevating context above prompting. Where it stays shallow: the framework treats context as a static input, when in a long-running coding session it's a moving target. The agent's own intermediate outputs pollute the window over time. You need context _compaction_, summarizing and pruning mid-task, which is a harder engineering problem than the explainer suggests.

Concept 2: Tool Use / Function Calling

Tool use, also called function calling, is how an agent acts on the world: you expose functions (run_tests, read_file, git_commit) with typed schemas, and the model emits a structured call the runtime executes. Without it, an agent can only talk. With it, an agent can ship.

The schema is the contract. A minimal one names the function (say, run_tests), gives a one-line description the model reads to decide _when_ to call it, and declares a typed parameter object: a single required string path, for instance. The model never sees your implementation, only that contract. A vague description is the most common reason an agent fires the wrong tool, so the schema's prose matters as much as its types.

This is the concept MCP standardized. Before MCP, every tool integration was bespoke per vendor. Anthropic introduced it in November 2024. By December 2025 it was a Linux Foundation project adopted across the three biggest labs. In 2026, if you're building tool use from scratch instead of speaking MCP, you're reinventing a wheel that three competitors already standardized.

The failure mode IBM glosses over is tool-call hallucination: the model invents an argument, calls a function that doesn't exist, or passes a path outside the repo. And tool use is the single largest security surface in agentic coding. An agent with shell access and a hallucinated rm is a bad afternoon. I've written a full tool approval pattern playbook and a tool-use security checklist precisely because the "just give it tools" framing skips the part where tools are also an attack vector. AI security is not an advanced topic here. It's concept zero.

Here's the official IBM walkthrough if you want the vendor's version alongside this critique:

Concept 3: Memory & State Management

Memory is how an agent carries information across steps and sessions. Short-term memory is the current task's working state. Long-term memory is facts, preferences, and prior decisions persisted to a store. State management is the discipline of keeping that memory consistent, scoped, and fresh.

For a multi-step coding task, short-term state is the chain of edits and test results so far. Long-term memory is where it gets interesting and dangerous. A pattern I use is a small persisted key-value scratchpad: at the end of a session the agent writes down what matters (the migration branch name, the list of test suites currently passing), and at the start of the next session it reads the whole scratchpad back to resume with prior context instead of rediscovering everything from scratch.

The failure mode is memory drift: stale facts outliving their truth. The agent "remembers" that the billing tests pass, three commits after they started failing. Multiply that across sessions and the agent operates on a fictional codebase, confidently building on a foundation that no longer exists. IBM names memory as a concept but doesn't grapple with invalidation, which is the actual hard part. Every persisted fact needs an expiry and a provenance trail: when was this written, by which run, and is it still true? A memory store without that is a slow-motion bug generator.

The fix I've settled on is to treat agent memory the way you'd treat a cache: short TTLs on anything derived from the live codebase, and re-verification (re-run the tests) rather than trust, before the agent acts on a remembered fact. This is one of those places where AI agents borrow directly from distributed-systems hygiene. If your agent keeps repeating fixed mistakes, memory hygiene is usually the culprit; I dug into that specific pattern in a post on preventing repeated AI coding mistakes.

Concept 4: Multi-Agent Orchestration

Multi-agent orchestration is coordinating several specialized agents (a planner, a coder, a reviewer, a tester) so each handles a sub-task and a controller routes work between them. It's the concept that sounds most impressive on a slide and is most often premature in practice.

There's a real version of this. The pipeline I run for this blog uses agent orchestration across seven distinct roles (research, copywriting, images, review, language, publishing, distribution) because those jobs genuinely have different shapes. The lesson I'd actually stand behind: model-per-job-shape beats one-model-everywhere. I run Sonnet for the tool-heavy loops and Opus for prose, and that split beat using one model for everything on both cost and quality.

But orchestration has a cost, and it's not linear. Every handoff adds latency, token overhead, and a new place for coordination to fail. Based on the cost-per-task math I ran for this site, a naive multi-agent setup can multiply token spend several times over a single-agent loop for the same task, because each agent re-establishes context the last one already had. Devin, Cognition Labs' autonomous engineer, is the public case study here: impressive demos, real skepticism about reliability on open-ended work. IBM presents orchestration as a capability ladder you climb. I'd present it as a cost you justify. Most teams in 2026 should run one well-instrumented agent before they run five.

Concept 5: Evaluation & Guardrails (the one IBM under-sells)

Evaluation is how you measure whether a non-deterministic agent's output is actually correct, safe, and good before it reaches your main branch. Guardrails are the automated constraints (permission boundaries, output validators, human-in-the-loop gates) that stop a bad run from doing damage. This is the concept vendor explainers consistently treat as an afterthought, and it's the one that decides whether agentic coding survives contact with production.

The problem is non-determinism. The same prompt yields different code on two runs, so you can't assert output == expected. You evaluate behavior: did the tests pass, did it touch only the files it should have, did it introduce a known vulnerability pattern. This is exactly the agent-evaluation gap the broader topic neighborhood keeps searching for and almost nobody covers well.

Here's the single most useful thing I've learned running an agent pipeline through 261+ published posts on this site: deterministic gates before LLM review catch more than doubling the size of the review model. My pipeline runs a hard-coded quality gate (word counts, banned phrases, link floors, schema validation) before any model reviews the draft. That cheap, boring, deterministic layer rejects more real defects than a bigger, more expensive reviewer ever did. As Simon Willison has argued repeatedly about LLM output, you verify what the model did, you don't trust that it did it.

For code agents, a minimal eval harness runs the agent against a fixed suite of tasks and scores observable outcomes, not exact text. For each case you assert three things: the test suite passes, the agent changed no more than the expected number of files, and a security scan over the diff returns no findings. Run that suite on every candidate change and you have a repeatable number, pass rate across N tasks, instead of a vibe about whether the agent "seems good." That number is what lets you compare models, prompts, and harness versions honestly.

If you want to go further, I've written up how to benchmark coding agents on your own repo and which AI code review tools actually catch bugs versus faking confidence. IBM lists evaluation fifth. In production, it's first.

Where IBM's framework holds up

The nouns are right. Context, tools, memory, orchestration, evaluation genuinely are the five load-bearing concepts, and naming them in plain language is a real service to developers drowning in agent hype. The framework is also pedagogically honest about the core shift: coding with AI agents is about system design, not prompt tricks. That matches my experience exactly. The teams doing this well treat it as distributed-systems engineering with a stochastic component, not as advanced autocomplete.

It's also well-timed. Dropping a named, numbered framework right as Claude Code, Cursor, and Copilot's agent mode hit mainstream workflows gives working engineers a shared vocabulary. Shared vocabulary is underrated.

Where IBM's framework oversimplifies

Three gaps. First, security is not a concept in the list, and it should be. Tool use is the attack surface. An agent with shell and network access is a new class of insider threat. Any framework that teaches tool use without teaching permission boundaries is teaching you to load a gun without the safety. The thing I learned the hard way on this predates agents: at Rise People in 2022 I built CLI tooling that scaffolded new projects already SOC 2-compliant, and the lesson was blunt. Compliance baked into scaffolding beats compliance review at PR time. The same logic maps directly onto agents. Bake permission boundaries and secret redaction into the harness the agent runs inside; don't bolt them on at review, when the destructive command has already executed.

Second, cost and team-workflow impact are absent. Orchestration and long context windows cost real money per task, and agentic workflows change how humans spend their day, sometimes for the worse. I've watched the productivity-panic burnout pattern hit teams that adopted agents without changing their review culture: the agent writes four times the code, and a human still has to read all of it. A concept framework that ignores the human loop is incomplete.

Third, evaluation is ranked last when it should anchor the whole thing. The ordering implies you learn evals after you've mastered orchestration. Backwards. You need an eval harness before you let an agent touch your repo, the same way you need tests before a refactor.

How to apply these 5 concepts in a real 2026 dev workflow

For a solo developer or small team, here's the minimal-viable path, in order:

1. Start with one agent, not a swarm. Pick a single agentic coding tool and learn its loop before you orchestrate anything.
2. Engineer context before you touch prompts. Scope what the agent sees to the relevant files. A tight context beats a clever prompt every time.
3. Speak MCP for tools. Use the standard so your integrations survive the next vendor shift, and gate every destructive tool behind human approval.
4. Give memory an expiry. Persist what resumes a session, invalidate what goes stale, and never let the agent trust its own old notes blindly.
5. Build the eval harness first. A fixed task suite with observable pass/fail checks is the difference between shipping agent code and praying over it.

Measure the whole thing. I keep a running set of metrics for AI coding impact because the only way to know if agents help is to instrument them. Vibe coding failed its critics because it skipped measurement. Don't repeat that with agents.

IBM's five concepts are a good map. But a map that leaves off the cliffs (security, cost, and the uncomfortable truth that evaluation comes first) will walk a confident team right off the edge. In 2026 the engineers who win with agents won't be the ones who memorized five nouns. They'll be the ones who wired the guardrails before they handed over the keys.

Photo by Nangialai Stoman on Unsplash.