Add agentic module: sequential, parallel, loop, supervisor and error-recovery workflows

Co-Authored-By: Claude Sonnet 5.5 <[email protected]> Claude-Session: https://claude.ai/code/session_01G8ikz8xdWuTP5yun8DZ1hk
2026-10-11 07:58:09 +00:00
parent f87573fcb8
commit da39f6437a
27 changed files with 832 additions and 1 deletions
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+Order the agents ran in:
+  findFlight read [traveler, destination] and wrote AI-101 Mumbai to Lisbon
+  findHotel read [traveler, destination] and wrote Hotel Alfama
+  findActivity read [traveler, destination] and wrote Tram 28
+  write read [hotels, activities, destination, flights] and wrote Fly AI-101, stay at Hotel Alfama, ride Tram 28.
+
+Shared state at the end:
+  traveler = Ankur
+  hotels = Hotel Alfama
+  activities = Tram 28
+  destination = Lisbon
+  flights = AI-101 Mumbai to Lisbon
+  plan = Fly AI-101, stay at Hotel Alfama, ride Tram 28.
+
+The prompt the planner was sent (last model call):
+  Write a one-line itinerary for Lisbon. Flight: AI-101 Mumbai to Lisbon. Hotel: Hotel Alfama. Activity: Tram 28.
+
+Model calls: 4
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+A) Planner declares only @V destination, template mentions {{flights}} {{hotels}} {{activities}}.
+   omit nothing: Value for the variable 'hotels' is missing
+   omit flights: Value for the variable 'hotels' is missing
+   omit hotels: Value for the variable 'hotels' is missing
+   omit activities: Value for the variable 'hotels' is missing
+
+B) Planner declares @V destination, @V flights, @V hotels, @V activities.
+   omit nothing: OK: Fly AI-101, stay at Hotel Alfama, ride Tram 28.
+   omit flights: Missing argument: flights
+   omit hotels: Missing argument: hotels
+   omit activities: Missing argument: activities
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+Three finders, each model call sleeps 300 ms.
+
+  #27            start    0 ms  end  301 ms
+  #31            start    1 ms  end  302 ms
+  #29            start    0 ms  end  302 ms
+
+Distinct threads: 3
+Wall clock for the whole sequence: 320 ms (sequential would be at least 900 ms)
+Plan: Fly AI-101, stay at Hotel Alfama, ride Tram 28.
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+Scorer replies 5, 7, 9, 10. Exit condition: score >= 8. maxIterations 5.
+
+testExitAtLoopEnd = false (default): final plan 'draft 3', scorer ran 3 times, improver ran 2 times, final score 9
+testExitAtLoopEnd = true:            final plan 'draft 4', scorer ran 3 times, improver ran 3 times, final score 9
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+Supervisor model was scripted to reply with three JSON decisions.
+
+Answer: Hotel Alfama
+
+Worker calls: 2
+  List one flight option to Lisbon for Ankur.
+  List one hotel option in Lisbon for Ankur.
+
+Supervisor model calls: 3
+
+First prompt the supervisor saw:
+The user request is: 'Find me a flight and a hotel for Lisbon'.
+The last received response is: ''.
+
+You must answer strictly in the following JSON format: {
+"agentName": (type: string),
+"arguments": (type: java.util.Map<java.lang.String, java.lang.Object>)
+}
+
+What each responseStrategy returned for the same three decisions:
+  (default)  Hotel Alfama
+  SCORED     Flight AI-101 and Hotel Alfama booked options found. (supervisor calls: 4)
+
+  The extra SCORED call asked the supervisor model:
+    You are a response evaluator that is provided with two responses to a user request.
+    Your role is to score the two responses based on their relevance for the user request.
+    
+    For each of the two responses, response1 and response2, you will return a score, respectively score 1 and score 2,
+    between 0.0 and 1.0, where 0.0 means the response is completely irrelevant to the user request,
+    and 1.0 means the response is perfectly relevant to the user request.
+    
+    Return only the score and nothing else, without any additional text or explanation.
+    
+    The user request is: 'Find me a flight and a hotel for Lisbon'.
+    The first response is: 'Hotel Alfama'.
+    The second response is: 'Flight AI-101 and Hotel Alfama booked options found.'.
+    
+    You must answer strictly in the following JSON format: {
+    "score1": (type: double),
+    "score2": (type: double)
+    }
+
+  SUMMARY    Flight AI-101 and Hotel Alfama booked options found. (supervisor calls: 3)
+  LAST       Hotel Alfama (supervisor calls: 3)
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+A) Supervisor model never says done, maxAgentsInvocations = 2.
+   returned: AI-101 Mumbai to Lisbon
+   worker calls: 2, supervisor calls: 2
+
+B) Supervisor model replies in prose instead of JSON.
+   threw: JsonParseException: Unrecognized token 'Sure': was expecting (JSON String, Number, Array, Object or token 'null', 'true' or 'false')
+ at [Source: REDACTED (`StreamReadFeature.INCLUDE_SOURCE_IN_LOCATION` disabled); line: 1, column: 1]
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+Two agents, both with a method named find. What the supervisor model was shown:
+
+SYSTEM: SystemMessage { text = "You are a planner expert that is provided with a set of agents.
+You know nothing about any domain, don't take any assumptions about the user request,
+the only thing that you can do is rely on the provided agents.
+
+Your role is to analyze the user request and decide which one of the provided agents to call next to address it.
+You return an agent invocation consisting of the name of the agent and the arguments to pass to it.
+
+If no further agent requests are required, return an agentName of "done" and an argument named
+"response", where the value of the response argument is a recap of all the performed actions,
+written in the same language as the user request.
+
+Agents are provided with their name and description together with a list of applicable arguments
+in the format {'name', 'description', [argument1: type1, argument2: type2]}.
+
+Decide which agent to invoke next, doing things in small steps and
+never taking any shortcuts or relying on your own knowledge.
+Even if the user's request is already clear or explicit, don't make any assumptions and use the agents.
+Be sure to query ALL necessary agents.
+
+The comma separated list of available agents is: '{'find$0', 'Finds a train', [destination: String]}, {'find$1', 'Finds a bus', [destination: String]}'.
+
+
+", attributes = {} }
+USER: UserMessage { name = null, contents = [TextContent { text = "The user request is: 'Get me to Lisbon'.
+The last received response is: ''.
+
+You must answer strictly in the following JSON format: {
+"agentName": (type: string),
+"arguments": (type: java.util.Map<java.lang.String, java.lang.Object>)
+}" }], attributes = {} }
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+FlakyAgent's model throws on its first N calls.
+
+no handler,           N=1: Attempt[outcome=threw IllegalStateException: model unavailable (attempt 1), modelCalls=1]
+handler retry(),      N=2: Attempt[outcome=returned 'AI-101 Mumbai to Lisbon', modelCalls=3] (handler invoked 2 times)
+handler result(".."), N=5: Attempt[outcome=returned 'no flight found, ask the traveler', modelCalls=1]
+handler throwException(), N=5: Attempt[outcome=threw IllegalStateException: model unavailable (attempt 1), modelCalls=1]