Most enterprise AI systems today still run on chunks. Paragraph boundaries. Token limits. Random slices of logs. That works for search. It starts to wobble when you ask an agent to reason about a journey that unfolded over time: a claim, an incident, a migration, an integration change.
So this post is about episodes.
Episodes are the missing abstraction between raw logs and business journeys. If you design them well, your agents stop “rediscovering” context and start behaving like they’ve actually been part of the work.
This is a longish post, so if you’re in a hurry, here are the key takeaways:
Episodes give you meaningful boundaries; chunks give you arbitrary slices. An episode log is the simplest “event-sourcing style” starting point for memory. Summary layers are your “materialised views” for token budgets. Timeline and contrast queries need first-class support, not prompt hacks. Entity-centric episodic memory is how you align agents to domain models and API catalogues. Consolidation is how episodic memory turns into organisational learning.
1. Why episodes, not chunks
Chunks are fine when the question is basically: “find me the paragraph that says X”.
Chunks fail when the question is: “what happened last time, and what changed?”
Because the unit of reasoning is no longer a paragraph. It’s a journey:
a claim from lodgement to settlement an incident from detection to post-incident review an integration change from design to deployment to customer impact
If you store memory as chunks, the agent has to reconstruct the story every time from fragments. That reconstruction is slow, expensive, and fragile.
An episode is a story with structure. It gives the system a stable handle to remember “what happened”, “why it happened”, and “what we decided”.
2. What is an episode in an enterprise context?
Definition
An episode is a coherent unit of experience with a beginning, middle, and end.
In enterprise terms, an episode is usually one of:
claim lifecycle customer support case production incident refactoring sprint system migration integration change / release
It typically has:
a stable ID participants timestamps events artefacts outcomes
Episode vs chunk vs log line
Log line: a single event “Service X timed out” Chunk: a slice of text “Paragraphs 3–4 of the incident report” Episode: the structured story spanning logs, chat, tickets, runbooks, dashboards, PRs, emails, decisions
Three domain examples
Insurance One claim journey from lodgement to settlement, including evidence, decisions, approvals, and exceptions. Integration and microservices One end-to-end integration change: design, contract changes, event mapping, deployment, consumer impacts, rollback decisions. Platform/SRE One major incident: detection, triage, mitigation, root cause, follow-ups, and “what we changed so it doesn’t happen again”.
3. Core design questions for episodic memory
Before patterns, lock in the design lens. Four questions do most of the work.
3.1 Episode boundaries
How do you know an episode starts and ends?
Common boundary strategies:
business events “ticket opened” / “ticket closed”, “claim lodged” / “claim settled” explicit user action “start design episode”, “close episode with decision” inactivity timeout “no activity for 30 days” system milestones “deployment complete”, “post-incident review published”
Rule of thumb: prefer business boundaries where possible. Timeouts are a fallback, not a primary model.
3.2 Episode identity
What is the stable identifier?
Examples:
claim number incident ID change request ID integration ID project code
And then the next question: how do you link sub-episodes?
A common pattern:
a “parent episode” for the programme/journey sub-episodes for incidents, changes, escalations, or phases
3.3 Episode structure
What fields are mandatory?
Minimum set:
participants (people/teams/agents) entities (customer/system/service/integration) timestamps (start/end, key milestones) status (open/closed) outcome (approved/declined/fixed/rolled back) evidence pointers (tickets, PRs, dashboards, files)
Artefacts need to be first-class, not “stuffed in text”.
3.4 Episode lifecycle
How long is the episode hot and frequently accessed?
Typical lifecycle:
hot: active work (hours/days/weeks) warm: referenced during follow-ups (weeks/months) cold: archived for audit/learning (months/years) redacted/anonymised/deleted: by policy
If you don’t define lifecycle early, you end up with accidental retention and accidental exposure.
4. Pattern 1: The Episode Log
Use when: you have a log-rich environment and want minimal episodic memory without redesigning everything.
Idea
Create an episode log: a higher-level stream where every record belongs to an episode and carries enough structure for retrieval and reasoning.
This is event-sourcing thinking, but with a business-friendly unit: the episode.
Minimal schema
episode_id timestamp event_type (message, tool_call, system_event, decision, artefact, summary) actor (user, agent, system) entities (customer/system/service/integration) payload (text + metadata + references)
Example (JSON):{ "episode_id": "INC-2026-01421", "timestamp": "2026-01-08T10:12:00+11:00", "event_type": "decision", "actor": "sre_lead", "entities": ["service:payments-api", "env:prod"], "payload": { "decision": "rollback", "rationale": "error rate spike after deployment 2026.01.08.3", "evidence": ["grafana:dash/123", "ticket:JIRA-8891", "commit:abc123"] } }
Construction
Map existing tickets/incidents/claims to episode_id Ingest logs and conversations, attach to episodes Ensure agents always write to the log with the correct episode_id
This is where orchestration vs choreography thinking shows up again. If multiple services are emitting events, you want a disciplined way to correlate them into one episode, otherwise you get a memory hairball.
Retrieval patterns
replay this episode (audit/explainability) give me a compressed view of the last N events (continuity) find episodes of type X with outcome Y (learning/analytics)
What to emphasise
You’re not forcing the agent to read raw logs. You’re exposing a structured, episode-centric stream that can be summarised, indexed, and governed.
5. Pattern 2: Episode Summary Layers
Use when: episodes can span weeks/months and are too heavy to load into context.
Idea
Maintain multiple summary layers per episode. Different tasks need different slices.
Think of these summaries as materialised views:
they are derived from the episode log they are cheaper to read than replaying everything they evolve over time
Summary types
Narrative summary What happened, in order, and why. Decision summary Decisions, rationale, approvals. Risk/exception summary Issues encountered, mitigations, residual risks. Metric summary Time to resolution, cost, number of changes, incident count.
How it works
On creation:
create an episode header who/what/why, starting state, key entities
As events accumulate:
update rolling summaries (per phase, per milestone) keep a “latest snapshot” summary for quick retrieval
On closure:
produce a final recap with stable summaries link to evidence pointers (tickets, PRs, dashboards)
Retrieval usage
For agents:
load decision summary + latest narrative snapshot before acting pull raw events only when evidence is needed
For humans:
faster handovers, audits, and post-incident reviews
This is how you keep token budgets under control without losing auditability.
6. Pattern 3: Timeline and Contrast Queries
Use when: questions involve change over time or comparisons across episodes.
Idea
Design episodic memory to support timeline and contrast queries as first-class operations.
If you don’t, the agent ends up doing “prompt archaeology”, and you get inconsistent answers.
Example queries
Within an episode:
what changed between initial plan and implemented solution which decisions were reversed, and why what did we believe at the start vs what we learned later
Across episodes:
compare this release’s incident profile to the last three releases how does this claim differ from similar claims that escalated what changed between the last two integration revisions
Implementation hints
State snapshots:
capture key snapshots at milestones before/after deployment, before/after a major design decision
Deltas:
store computed differences config diffs, topology diffs, contract diffs, policy diffs
Query functions:
summarise_changes(episode_id, t1, t2) compare_episodes(e1, e2) list_reversed_decisions(episode_id)
These functions do the heavy lifting outside the prompt, so the agent receives focused, structured inputs.
7. Pattern 4: Entity-Centric Episodic Memory
Use when: you have strong domain entities and want agents to think “per entity”, not just “per episode”.
Idea
Link episodes to entities so you can pivot:
from “this journey” to “this entity’s history”
This is where domain-driven design and API catalogues become operational memory, not just documentation.
Design elements
Entity index:
for each entity (customer, system, service, integration), keep: associated episode IDs episode types key summaries last outcomes recent risk flags
Typical questions:
what episodes has this customer gone through in the last 12 months which incidents and architecture changes involve this system what decisions have we made about this domain boundary over time
Agent usage:
before making a decision about an entity: load top relevant episodes (recent + similar outcomes) load semantic constraints (policies, standards, ownership) then plan
If you only do episode-centric retrieval, agents can still miss “this customer always needs exception X” or “this service has a recurring failure mode after schema changes”.
8. Pattern 5: Consolidation into Semantic Memory
Use when: you want the organisation to learn from episodes, not just store them.
Idea
Episodes are raw material. Semantic memory is the distilled organisational truth:
facts relationships policies heuristics known patterns and mitigations
Consolidation flows
After episodes close:
extract durable facts true dependencies, stable mitigations, recurring failure chains update a structured store or knowledge graph
Periodic consolidation:
batch scans closed episodes and proposes updates: “incidents of type X often involve service Y” “claims with profile P tend to escalate unless we do Q” “integration changes involving system Z require additional validation”
Why this matters
This is the loop that prevents “fresh slate syndrome”.
Instead of treating each episode as a new world, agents inherit distilled knowledge shaped by prior outcomes, and humans get a continuously updated view of how the organisation actually operates.
9. Practical steps to introduce episodic memory
Start small. Design for reuse. Avoid a platform rebuild.
Choose one domain and define episodes Examples: incident, claim, deployment, integration change. Stand up an episode log Minimal schema, backfill last 3–6 months from tickets/logs/chat. Add summary layers Start with narrative summary. Add decision and risk summaries once you feel the pain. Wire one agent to use episodes Before acting: load summaries for current episode and entity. After acting: write events and update summaries. Iterate with governance and analytics Use episodic memory in post-incident reviews, audits, and design reviews. Capture the questions you wish you could answer, then evolve the schema deliberately.
Summary
Episodic memory is not a research concept. It maps cleanly to how enterprises already operate:
journeys cases incidents changes decisions outcomes
If you design episodes well:
agents stop “rediscovering” constraints humans get better auditability and faster handovers and your semantic layer starts reflecting real behaviour, not just diagrams
Next post: metrics and observability for episodic memory — how to measure recall quality, drift, and cost, without turning your memory stack into a data swamp.
Alok Mishra 