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Goal Timeline

What the Goal Timeline shows​

Every goal execution produces a detailed timeline — a chronological log of every tool call the agent made, the arguments it used, the result it got, and how long it took. This is stored in the goal_action_log table and accessible from the Goal Progress panel.

The timeline answers: "What exactly did the agent do to produce this result?"

Accessing the timeline​

From the Goal Progress panel:

While a goal is running (or after it completes), click View Timeline at the bottom of the goal panel.

From the chat:

After a goal completes, a "View full execution log" link appears in the completion message.

Via API:

GET /api/autonomous/goal/:id/timeline

Reading the timeline​

Goal: "Research NVIDIA Q1 2026 earnings and create a slide deck"
Status: Completed  •  8 iterations  •  47.2 seconds

──────────────────────────────────────────────────────────
[Iteration 1]  09:15:32  ──────────────────────────  0.8s
  Action:   web_search
  Args:     { "query": "NVIDIA Q1 2026 earnings results" }
  Result:   8 results returned
  Tokens:   412 prompt / 89 completion

[Iteration 2]  09:15:33  ──────────────────────────  1.4s
  Action:   web_fetch
  Args:     { "url": "https://investor.nvidia.com/..." }
  Result:   Revenue: $26.0B (+262% YoY), EPS: $5.98 (beat by $0.12)
  Tokens:   1,847 prompt / 156 completion

[Iteration 3]  09:15:35  ──────────────────────────  1.1s
  Action:   web_search
  Args:     { "query": "NVIDIA Q1 2026 analyst reactions Wall Street" }
  Result:   6 results returned

[Iteration 4]  09:15:36  ──────────────────────────  0.9s
  Action:   web_fetch
  Args:     { "url": "https://seekingalpha.com/..." }
  Result:   Analyst consensus: Buy. Price target raised to $1,100.

[Iteration 5]  09:15:38  ──────────────────────────  3.2s
  Action:   execute_python
  Args:     { "script": "# Structure earnings data as JSON\n..." }
  Result:   { "revenue": 26.0, "eps": 5.98, "yoy_growth": 262, ... }

[Iteration 6]  09:15:42  ──────────────────────────  6.1s
  Action:   generate_powerpoint
  Args:     { "title": "NVIDIA Q1 2026 Earnings", "slides": [...] }
  Result:   File created: nvidia_q1_2026.pptx (8 slides, 2.4 MB)

[Iteration 7]  09:15:48  ──────────────────────────  0.4s
  Action:   validate_file
  Args:     { "path": "nvidia_q1_2026.pptx" }
  Result:   File exists, 8 slides, non-empty ✅

[Iteration 8]  09:15:49  ──────────────────────────  0.3s
  Assessment: All success criteria met
  → Goal COMPLETED
──────────────────────────────────────────────────────────
Total: 8 iterations  47.2s  2,847 prompt tokens  512 completion tokens  ~$0.03

Self-correction in the timeline​

When a script fails and self-corrector fires, it appears as extra iterations:

[Iteration 5]  ──────────  2.1s  ❌ FAILED
  Action:   execute_python
  Result:   AttributeError: 'NoneType' object has no attribute 'text'

[Iteration 5a] ──────────  1.8s  (self-correction)
  SelfCorrector analyzing error...
  Diagnosis: price element selector changed — updated XPath

[Iteration 5b] ──────────  2.4s  ✅ RETRY
  Action:   execute_python  (corrected script v2)
  Result:   Price: ₹2,847.50

Self-corrections are labeled with letter suffixes (5a, 5b, 5c) and include the diagnosis the SelfCorrector produced.

Timeline filtering​

The timeline panel has filters:

  • Show all — every iteration including successful ones
  • Errors only — iterations that failed or triggered self-correction
  • File operations — iterations that created, read, or wrote files
  • External calls — iterations that called external APIs (web, integrations)

Using the timeline for debugging​

If a goal produced an unexpected result, the timeline shows exactly where it went wrong:

  1. Find the first iteration marked with ❌
  2. Expand the full result — often the actual error message is there
  3. Look at the args — did the agent use the right query/URL/parameters?
  4. Check the self-correction attempt — did it diagnose the error correctly?

Common patterns:

  • Web scraping failures: the page structure changed, or the site returned a CAPTCHA — look for "selector not found" in iteration results
  • Wrong data: the agent found results for the wrong ticker/company — the search query may need more specificity in your goal
  • Timeout at iteration 40: the goal hit the max iteration limit — try breaking it into smaller sub-goals

Comparing timelines across runs​

Run the same goal twice and compare timelines to see:

  • Whether the skill learned from the first run made the second faster
  • Whether a self-correction produced a better script version
  • Token usage trends across iterations (useful for cost optimisation)

Each goal has a unique goalId — access historical timelines via:

GET /api/autonomous/goal?status=completed&limit=20
# Then fetch any goal's timeline:
GET /api/autonomous/goal/:id/timeline