1 00:00:00,350 --> 00:00:07,070 When an AI reads a file, uses a tool, and checks a result, the model is only part of what you're seeing. 2 00:00:07,250 --> 00:00:10,050 The surrounding software makes that work possible. 3 00:00:10,230 --> 00:00:13,030 That surrounding system is called a harness. 4 00:00:13,210 --> 00:00:18,090 I'm going to show you one real task, and point out the pieces that make an agent useful. 5 00:00:18,890 --> 00:00:24,650 I'm using DeepSeek Harness, a real agent app with a conversation and tools for files. 6 00:00:24,830 --> 00:00:28,030 The model is one component inside this application. 7 00:00:28,210 --> 00:00:34,130 The instructions, available tools, and permission settings all affect what the complete system can do. 8 00:00:34,310 --> 00:00:39,670 The name in the model selector does not tell you everything about what the complete system can accomplish. 9 00:00:39,850 --> 00:00:43,130 The model chooses what to say or which action to request. 10 00:00:43,310 --> 00:00:46,910 A tool does something outside the model, such as reading a file. 11 00:00:47,090 --> 00:00:48,770 The harness connects those pieces. 12 00:00:48,950 --> 00:00:55,270 It supplies context, makes allowed tools available, runs permitted actions, and returns their results. 13 00:00:55,450 --> 00:00:59,450 The model can then use that new information to decide what to do next. 14 00:00:59,630 --> 00:01:02,990 For this task, I have a small file of fictional expenses. 15 00:01:03,170 --> 00:01:10,050 The goal is to calculate spending by category, flag anything that needs attention, and create a short summary. 16 00:01:10,230 --> 00:01:13,110 First, look at the information we are giving the agent. 17 00:01:13,290 --> 00:01:15,690 The file contains the original records. 18 00:01:18,247 --> 00:01:25,447 The instructions say how to handle a blank category: keep the record, group it separately, and do not guess. 19 00:01:28,004 --> 00:01:31,684 We also tell it to preserve the input instead of quietly changing the source. 20 00:01:31,864 --> 00:01:36,664 These details are context: the information available to the model for this job. 21 00:01:36,844 --> 00:01:39,484 More context is not automatically better. 22 00:01:39,664 --> 00:01:43,504 Unrelated files and old conversations can distract from the task. 23 00:01:43,684 --> 00:01:50,004 A useful harness helps select the relevant instructions and evidence, within the scope we allow. 24 00:01:50,184 --> 00:01:54,664 For longer tasks, a harness also has to manage limited context space. 25 00:01:54,844 --> 00:01:58,524 It may select earlier results or summarize older conversation. 26 00:01:58,704 --> 00:02:03,184 Summaries can lose detail, so keep critical facts in an artifact you can reopen. 27 00:02:03,364 --> 00:02:09,204 The file is durable; whether the next response uses it depends on what the system actually loads. 28 00:02:09,384 --> 00:02:16,264 Before pressing send, check that the request names the input, the expected output, and the rule for uncertainty. 29 00:02:16,444 --> 00:02:19,244 That makes success something we can actually inspect. 30 00:02:19,784 --> 00:02:21,224 Now we submit the task. 31 00:02:21,404 --> 00:02:24,764 Watch the activity between the prompt and the final response. 32 00:02:24,944 --> 00:02:28,784 The agent requests a tool, and the application performs the action. 33 00:02:28,964 --> 00:02:34,084 Opening the tool details lets us see which file it read and what information came back. 34 00:02:36,642 --> 00:02:38,402 We can see what it actually read. 35 00:02:38,582 --> 00:02:41,302 The model uses that result to choose its next step. 36 00:02:41,482 --> 00:02:46,042 If it needs another file or a calculation, another tool request can follow. 37 00:02:46,222 --> 00:02:51,982 This is the basic agent loop: decide, act through a tool, inspect what happened, and continue. 38 00:02:52,162 --> 00:02:54,882 This example uses files and a calculation. 39 00:02:55,062 --> 00:03:01,862 Those capabilities come from the integration around the model, and they should be limited to what the task needs. 40 00:03:02,042 --> 00:03:06,522 Installing a connector does not mean the agent should have unrestricted access. 41 00:03:06,702 --> 00:03:09,822 The loop does not guarantee that every decision is correct. 42 00:03:10,002 --> 00:03:14,962 It gives the system a way to work with evidence instead of stopping at a single response. 43 00:03:15,142 --> 00:03:20,822 In this example, the useful evidence is the original expense data and the calculations made from it. 44 00:03:21,002 --> 00:03:25,722 We can follow the activity and compare the proposed summary with those records. 45 00:03:25,902 --> 00:03:31,742 Notice that an empty category is still something to flag, not an invitation to invent a label. 46 00:03:31,922 --> 00:03:35,202 Check whether the actions and results support the answer. 47 00:03:35,382 --> 00:03:38,902 Before accepting the summary, we check it against the original data. 48 00:03:39,082 --> 00:03:45,962 First, the calculator totals the source records. At this stage, it correctly reports that no summary has been saved. 49 00:03:46,142 --> 00:03:49,982 The expense with a missing category must remain visible in that check. 50 00:03:50,162 --> 00:03:55,442 Twelve records total two hundred and twenty-seven, including seventeen without a category. 51 00:03:57,999 --> 00:04:02,959 The calculated totals match the source records; that is evidence for this calculation. 52 00:04:03,139 --> 00:04:04,979 Now look at the write boundary. 53 00:04:05,159 --> 00:04:09,079 Reading the input and saving a new file are different actions. 54 00:04:09,259 --> 00:04:15,259 The first write is blocked by the read-only setting. The agent then asks for one-time permission to save this summary. 55 00:04:15,439 --> 00:04:22,719 Here, the application restricts which files and command the agent can use, and asks before writing this summary. 56 00:04:22,899 --> 00:04:26,099 This demonstrates an application permission boundary. 57 00:04:26,279 --> 00:04:31,399 An operating-system sandbox is a separate boundary, and we have not demonstrated one here. 58 00:04:31,579 --> 00:04:35,979 Inspect the proposed destination and contents before allowing this specific action. 59 00:04:38,536 --> 00:04:43,096 After the file is saved, reopen it and confirm what actually exists. 60 00:04:43,276 --> 00:04:50,476 The checker compares the saved category totals, grand total, and the count of records missing a category against the source. 61 00:04:50,656 --> 00:04:55,536 A pass supports those checks; it does not verify every sentence in the report. 62 00:04:55,716 --> 00:04:59,556 That final readback matters more than a message saying the work is done. 63 00:05:02,113 --> 00:05:06,433 Saved files only become context when the system loads them again. 64 00:05:10,113 --> 00:05:13,473 Approvals and sandboxes also do different jobs. 65 00:05:13,653 --> 00:05:16,693 An approval asks whether an action may proceed. 66 00:05:16,873 --> 00:05:20,393 A sandbox limits what the execution environment can reach. 67 00:05:20,573 --> 00:05:22,893 Always check what is actually enforced. 68 00:05:23,073 --> 00:05:29,393 The original file is unchanged, the totals have been checked, and the saved summary can be reopened. 69 00:05:31,950 --> 00:05:35,950 Following those steps gives us a practical way to evaluate the system. 70 00:05:36,130 --> 00:05:39,490 When you evaluate an agent, ask four questions. 71 00:05:39,670 --> 00:05:41,430 What information can it see? 72 00:05:41,610 --> 00:05:43,130 Which actions can it take? 73 00:05:43,310 --> 00:05:44,590 How is the result checked? 74 00:05:44,770 --> 00:05:46,210 And where must it stop? 75 00:05:46,390 --> 00:05:50,230 Those questions tell you much more than the name of the model alone. 76 00:05:50,410 --> 00:05:52,890 Try this with one small task of your own. 77 00:05:53,070 --> 00:05:59,550 Follow the actual steps, inspect the output, and identify one check you would want before trusting the result.