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An AI agent is not merely a model that emits a long answer. It is an interactive think–act–observe loop: the system chooses an action, receives an environmental result that was unavailable beforehand, and uses that result to choose what happens next. CodeSmithi’s architecture makes that distinction concrete, while its five-wave model explains how agent engineering has progressed from prompts to complete execution graphs.
What an AI agent is really doing
The defining property of an agent is feedback. A model may propose a command, call a tool, inspect the result, and alter its next action. The environment—not just the original prompt—changes the information available to the system.
The think–act–observe loop
- Think: interpret the task and decide whether an action is needed.
- Act: call a tool, edit a file, run a test, query a service, or perform another permitted operation.
- Observe: read the resulting output, error, state change, or interruption.
- Update: use that new evidence to continue, revise the plan, or stop.
Compiler output, a failed test, a missing file, or an unavailable tool can all redirect the next step. As DogeKing puts it, “An Agent’s action trajectory cannot be reduced to one longer static answer.”
Why a longer prompt is not enough
A static prompt can contain instructions and background facts, but it cannot contain the result of an action that has not happened yet. If success depends on compiling code, inspecting a live file, or querying an external system, the system must interact with that environment. Otherwise, it can produce a plausible explanation without completing the task.
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A practical test for inflated agent claims
Ask whether an unexpected result can change the next action. If the sequence is predetermined and cannot branch on observations, the system is closer to a fill-in-the-blank prompt or fixed workflow than to an interactive agent.
- One stateless API call: no feedback loop exists.
- Fixed workflow: predefined steps may be useful, but the sequence does not genuinely adapt.
- Long error-prone chain: more steps do not create autonomy when mistakes simply propagate.
- Unexecuted claims: saying that tests passed is not the same as running them and reading their output.
- Model-only capability claims: performance also depends on tools, context, constraints, and recovery paths.
- Anthropomorphic language: saying an agent “decided” can hide the concrete loop, permissions, and evidence behind its behavior.
How CodeSmith implements a ReAct tool loop
The reference CodeSmith source is version v0.5.0 at commit 3a74c82f. Its DefaultAgentExecutor::run_inner method follows the ReAct pattern: assemble a message request, stream the model response, collect tool calls, execute them, append the results to history, and repeat until the model stops requesting tools.
What happens on each iteration
- The executor builds the next message request from the current conversation and available tool definitions.
- The model response is streamed and inspected for tool calls.
- Requested tools are executed under the executor’s permissions and limits.
- Each result is inserted into the conversation history.
- The model receives that updated history and either requests another action or ends the run.
CodeSmith represents tool results under the user role. If the model requests a nonexistent tool, the executor returns a NotAvailable result to the model instead of immediately crashing. That feedback gives the model an opportunity to correct the request.
Stop conditions are part of the agent
The executor exposes four terminal outcomes:
| Stop value | Meaning |
|---|---|
NoToolCalls |
The model produced a response without requesting another tool. |
MaxSteps |
The run reached its configured step ceiling; the article reports a default of 50 steps. |
Error(String) |
An error ended execution, with a diagnostic string. |
Interrupted |
Execution was stopped externally. |
A stop policy prevents an agent from running forever, but it also affects quality: stopping too early can cut off verification, while allowing unlimited retries can waste resources or repeat a failure.
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Context engineering: what the model can see changes what it can do
Agent context is more than a prompt. CodeSmith’s context analysis separates four components whose functions are easy to confuse.
| Context component | Primary function | What is lost when it is missing |
|---|---|---|
| Tool definitions | Describe available actions, parameters, and constraints. | The model may know what should happen but cannot express a valid action. |
| Tool execution results | Close the loop with observations from the environment. | The model acts blindly and cannot reliably verify or correct itself. |
| Reasoning | Records why an action was selected and can expose a mistaken assumption. | Decisions become harder to inspect and recover from. |
| Message history | Preserves prior actions, results, and failed attempts. | The system may repeat operations or forget what already failed. |
A damaged context can still produce a polished reply. Producing text is therefore not evidence that the task was completed.
The interface is part of capability
SWE-agent is used as an example of how the same foundation model can perform differently with a plain shell versus a purpose-designed Agent–Computer Interface. File presentation, edit commands, and error messages shape which actions are easy to choose and how quickly the model can recover. The model’s weights are only one part of the effective system.
Five levels of autonomy
Autonomy is a continuum, not a binary label. The five-level scale below describes who chooses actions and who can change the objective or plan.
| Level | Who controls the next move? | Defining capability |
|---|---|---|
| 1 | Developer | The developer specifies every action and its order. |
| 2 | Model within developer tools | The model selects actions from an allowed tool set. |
| 3 | Model with feedback | The model revises its plan after surprises or failed actions. |
| 4 | Model at the subgoal level | The model proposes and decomposes subgoals. |
| 5 | Model and task definition | The model examines the task, goals, and evaluation criteria themselves. |
CodeSmith is described as between levels 2 and 3: it selects tools and can be pushed toward verification and replanning, but it is not presented as a system that independently redefines the task.
ReAct, Reflexion, LATS, Voyager, and MemGPT
Different agent loops distinguish themselves by what they save, what they read later, and what triggers another round.
| Approach | What is saved | What is read | What triggers the next round |
|---|---|---|---|
| ReAct | No extra cross-step memory beyond the active interaction. | Current messages and tool observations. | The next observation or tool request. |
| Reflexion | A reflection recorded after failure or an unsuccessful attempt. | Past reflections alongside the current task. | A failure that calls for a revised approach. |
| LATS | Branches in a search tree. | Alternative trajectories and their evaluations. | Backtracking or selecting another branch. |
| Voyager | Successful, reusable skills. | Relevant skills for a new situation. | A task that can be solved by composing or extending a skill. |
| MemGPT | Layered memory managed through paging. | The memory layer brought into the active context. | A context or memory-management decision. |
These are design choices, not a universal progression. Extra memory can improve continuity but also increases retrieval, stale-information, and context-management costs.
Why the harness can matter more than the model
The harness is the operational layer around the model: tools, permissions, context assembly, execution checks, verification, retry limits, recovery behavior, and logs. A stronger model in a weak harness can underperform a similar model given clear interfaces and reliable feedback.
Verification turns claims into evidence
Automatic execution checks, repetitive-loop detection, and strategy refinement are examples of harness changes that can improve results without changing the underlying model. The article reports a LangChain Terminal Bench 2.0 increase from 52.8% to 66.5% in 2026 after such harness changes; it says the model was not swapped. This is a reported benchmark result, not a guarantee that every harness change will produce the same gain.
Useful harness controls
- Expose tools with precise schemas and permission boundaries.
- Return actionable errors, including unavailable-tool feedback.
- Run tests or other checks instead of trusting completion text.
- Detect repeated actions and stop or redirect them.
- Set step, time, compute, and data-access ceilings.
- Record the trajectory so a human can inspect decisions and observations.
- Define interruption and recovery behavior before deployment.
When multiple agents help—and when they do not
Adding agents does not automatically add intelligence. Multi-agent systems work best when the task decomposes into subtasks that are sufficiently independent and when coordination explicitly handles dependencies.
What a delegation contract should contain
- Objective: the exact outcome the delegated agent must produce.
- Tool permissions: which actions and data are allowed.
- Forbidden actions: operations that are out of scope.
- Resource ceilings: limits for time, steps, compute, or tokens.
- Abort conditions: when the delegate must stop and report.
- Output format: a machine- or human-readable result contract.
- Responsibility boundaries: who owns decisions and side effects.
- Renegotiation mechanism: how the delegate requests clarification or changed authority.
Coordination risks
Coordination gains are not monotonic with team size. More agents create communication and dependency overhead, while same-origin agreement is not independent evidence: agents exposed to the same prompt, context, or model failure can converge on the same wrong answer. Debate can also amplify an early anchor instead of correcting it.
For design comparisons, assess autonomy, memory strategy, replanning after environmental feedback, tool-interface quality, verification and stop conditions, delegation-contract completeness, coordination cost, and the transparency of logs and decision trajectories.
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The five waves of agent engineering
The waves are nested. Each adds a new control surface without making the earlier one irrelevant.
| Wave | Primary question | What engineers optimize |
|---|---|---|
| Prompt engineering | What should the model be told? | Natural-language instructions, examples, and task framing. |
| Context engineering | What should the model see? | Tools, history, observations, retrieved information, and reasoning context. |
| Harness engineering | How can the model act safely and recover? | Interfaces, constraints, verification, feedback, retries, and failure handling. |
| Loop engineering | How does operation continue across turns? | Autonomous iteration, memory, replanning, and decisions about when to verify or stop. |
| Graph engineering | How do all execution components fit together? | Loops, deterministic programs, human approvals, dependencies, and handoffs in an execution graph. |
Why the waves matter
A better prompt cannot compensate for a missing tool result. Better context cannot compensate for unsafe permissions or absent verification. A robust loop still needs an execution graph when deterministic code, specialist agents, and human approvals must interact. The competitive advantage therefore moves outward from model weights toward interfaces, context, harnesses, and the graph that governs execution.
How to evaluate an agent design
Before calling a system autonomous, inspect its behavior rather than its marketing label.
- Identify the environmental observations that can change the next action.
- List every tool, permission, resource limit, and forbidden operation.
- Check whether tool results and failures return to the model in usable form.
- Inspect how history, reflections, skills, or other memory are stored and retrieved.
- Measure whether the system replans after a surprise instead of repeating the same path.
- Verify completion with executable checks where possible.
- Review stop conditions, interruption handling, and recovery paths.
- For multi-agent systems, test the delegation contract and dependency handoffs.
- Read the logs as a trajectory: what was attempted, what was observed, and why the next action followed.
The takeaway
CodeSmithi is useful as a textbook anatomy because it exposes the machinery behind an agent: a ReAct loop, tool feedback, context assembly, stop conditions, and a bounded form of autonomy. Its five-wave framework places that machinery in a larger evolution—from better prompts to context, harnesses, loops, and finally execution graphs. The central question is simple: when the environment disagrees with the plan, can the system observe the disagreement and change what it does next?
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